Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> We propose a generic mixed integer linear programming model to minimize the operating cost of a residential microgrid. We model supply and demand of both electrical and thermal energy as decision variables. The modeled microgrid is operated in grid-connected mode. It covers solar energy, distributed generators, energy storages, and loads, among them controllable load jobs released by home appliances and electric vehicles. We propose a model predictive control scheme to iteratively produce a control sequence for the studied microgrid. Our case study reveals the performance of minimum cost control by comparison with benchmark control policies. We consider three price scenarios in our analyses which include two market-based scenarios. Numerical results from our study indicate savings in annual operating cost between 3.1 and 7.6 percent. <s> BIB001 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Abstract Renewable energy sources (RES) are an “indigenous” environmental option, economically competitive with conventional power generation where good wind and solar resources are available. Hybrid systems can help in improving the economic and environmental sustainability of renewable energy systems to fulfill the energy demand. The aim of this paper is to present a dynamic model able to integrate different RES and one storage device to feed a “Green” building for its thermal and electrical energy needs in a sustainable way. The system model is embedded in a dynamic decision model and is used to optimize a quite complex hybrid system connected to the grid which can exploit different renewable energy sources. A Model Predictive Control (MPC) is adopted to find the optimal solution. The optimization model has been applied to a case study where electric energy is also used to pump water for domestic use. Optimal results are reported for two main cases: the presence/absence of the energy storage system. <s> BIB002 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Energy management in Smart Home environments is undoubtedly one of the pressing issues in the Smart Grid research field. The aim typically consists in developing a suitable engineering solution able to maximally exploit the availability of renewable resources. Due to the presence of diverse cooperating devices, a complex model, involving the characterization of nonlinear phenomena, is indeed required on purpose. In this paper an Hybrid Soft Computing algorithmic framework, where genetic, neural networks and deterministic optimization algorithms jointly operate, is proposed to perform an efficient scheduling of the electrical tasks and of the activity of energy resources, by adequately handling the inherent nonlinear aspects of the energy management model. In particular, in order to address the end-user comfort constraints, the home thermal characterization is needed: this is accomplished by a nonlinear model relating the energy demand with the required temperature profile. A genetic algorithm, based on such model, is then used to optimally allocate the energy request to match the user thermal constraints, and therefore to allow the mixed-integer deterministic optimization algorithm to determine the remaining energy management actions. From this perspective, the ability to schedule the tasks and allocate the overall energy resources over a finite time horizon is assessed by means of diverse computer simulations in realistic conditions, allowing the authors to positively conclude about the effectiveness of the proposed approach. The degree of realism of the simulated scenario is confirmed by the usage of solar energy production forecasted data, obtained by means of a neural-network based algorithm which completes the framework. <s> BIB003 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Abstract Microgrid works as a local energy provider for domestic buildings to reduce energy expenses and gas emissions by utilising distributed energy resources (DERs). The rapid advances in computing and communication capabilities enable the concept smart buildings become possible. Most energy-consuming household tasks do not need to be performed at specific times but rather within a preferred time. If these types of tasks can be coordinated among multiple homes so that they do not all occur at the same time yet still satisfy customers’ requirement, the energy cost and power peak demand could be reduced. In this paper, the optimal scheduling of smart homes’ energy consumption is studied using a mixed integer linear programming (MILP) approach. In order to minimise a 1-day forecasted energy consumption cost, DER operation and electricity-consumption household tasks are scheduled based on real-time electricity pricing, electricity task time window and forecasted renewable energy output. Peak demand charge scheme is also adopted to reduce the peak demand from grid. Two numerical examples on smart buildings of 30 homes and 90 homes with their own microgrid indicate the possibility of cost savings and electricity consumption scheduling peak reduction through the energy consumption and better management of DER operation. <s> BIB004 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> A growing number of households are seeking energy autonomy and economic benefits by installing micro-CHP and PV generators, as well as battery storage units in their so-called smart homes. An option to further increase benefits, is to install a community microgrid and coordinate smart homes intelligently. To quantify this increase, we apply numerical simulations using real-world data for household loads in a temporal resolution of 15-minutes. In systems consisting of CHP-units, the degree of electricity autonomy rises from 50% to 80% through installing a microgrid, allowing lucrative CHP operation. In PV-based systems, the benefits are fewer and if battery storage is installed additionally, they almost disappear completely. As a consequence, intelligently managed microgrids are as valuable option for the integration of microgeneration as long as decentralized battery storage is not profitable and thus not employed. <s> BIB005 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> The optimization of energy consumption, with consequent costs reduction, is one of the main challenges in present and future smart grids. Of course, this has to occur keeping the living comfort for the end-user unchanged. In this work, an approach based on the mixed-integer linear programming paradigm, which is able to provide an optimal solution in terms of tasks power consumption and management of renewable resources, is developed. The proposed algorithm yields an optimal task scheduling under dynamic electrical constraints, while simultaneously ensuring the thermal comfort according to the user needs. On purpose, a suitable thermal model based on heat-pump usage has been considered in the framework. Some computer simulations using real data have been performed, and obtained results confirm the efficiency and robustness of the algorithm, also in terms of achievable cost savings. <s> BIB006 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> This paper addresses a hybrid renewable system that consists of a micro-Combined Cooling Heat and Power (CCHP) unit and a solar energy conversion device. In addition to a traditional PV system, a High Concentrator Photovoltaic (HCPV) device, the design of which is suitable for building integration application, was also modelled and embedded in the hybrid system. The work identifies the optimal management strategies for the hybrid renewable system in an effort to minimise the primary energy usage, the carbon dioxide emissions and the operational costs for variable electricity prices that result from the day-ahead electricity market. An "ad hoc" model describes the performance of the HCPV module, PV and Internal Combustion Engine, whilst the other units were simulated based on their main characteristic parameters. The developed algorithm was applied to three different building typologies. The results indicate that the best configuration is the hybrid renewable system with PV, which can provide a yearly primary energy reduction of between 20% and 30% compared to separate production. The hybrid renewable system with HCPV becomes competitive with the PV technology when the level of solar radiation is high. <s> BIB007 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Balancing power is used to quickly restore the supply-demand balance in power systems. The need for this tends to be increased by the use of variable renewable energy sources (VRE) such as wind and solar power. This paper reviews three channels through which VRE and balancing systems interact: the impact of VRE forecast errors on balancing reserve requirements; the supply of balancing services by VRE generators; and the incentives to improve forecasting provided by imbalance charges. The paper reviews the literature, provides stylized facts from German market data, and suggests policy options. Surprisingly, while German wind and solar capacity has tripled since 2008, balancing reserves have been reduced by 15%, and costs by 50%. <s> BIB008 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Abstract A model predictive control (MPC) based coordinated operation framework for a grid-connected residential microgrid with considering forecast errors is presented in this paper. This residential microgrid composes renewable energy resources (e.g., wind and solar), distributed generators (e.g., CHP), energy storages (e.g., battery bank and water tank), electrical vehicle, and smart loads (e.g. HVAC and washing machine). A novel mixed integer linear programming (MILP) problem is optimized at each decision time, on the basis of the short-term forecasts of renewable energy resources generation, load demand, and electricity price. This MILP problem is integrated into a MPC framework to reduce the negative impacts of forecast errors. Case study which considers forecast uncertainties is implemented for evaluating the performance of the proposed method and the traditional method is used. Besides, peak power price mechanism which is used to smooth the power exchanged with external grid is also considered. Moreover, a further sensitivity analysis is realized in order to discuss the impacts of energy storage units on the microgrid operation. Simulation results show that the proposed method is economic and flexible. <s> BIB009 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Aggregating groups of loads and generators at the same location with centralized control is known as the concept of microgrids. However, if those flexible producers and consumers do not have the ability to balance the variability and uncertainty of RES (renewable energy sources) production within them, from the system perspective they are seen as a source of imbalances and potential problems in maintaining the equilibrium of production and consumption. The papers main goal is to quantify the ability of microgrid components to provide flexibility. This flexibility is analysed from two perspectives, defining two operating principles of each microgrid: independently from the distribution grid and connected, interacting and responding to signals from the upstream system. Following on this, the paper presents two relevant cases. <s> BIB010 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> The development of technologies such as micro turbines and gas furnaces has been the major driving force towards integration of electricity and natural gas networks in the EH (energy hubs). Besides, the existing power grids are getting smarter by implementing the new generation of information technologies and communication systems. In smart grid, the electricity suppliers can modify the customers' electrical load consumption by employing appropriate scheduling schemes such as DR (demand response) programs. In this paper, we consider the S. E. Hubs (smart energy hubs) framework, in which the customers can use EMS (energy management system) to access to the electricity and natural gas prices data and wisely manage their daily energy consumption. We extend the existing DR programs to the IDR (integrated demand response) programs with the aim of modifying both electricity and natural gas consumption on the customer side. The interaction between S.E. hubs in the IDR program is formulated as a non-cooperative game. The goal of the IDR game is to maximize the natural gas and electricity utility companies' profit and to minimize the customers' consumption cost. It is shown that the proposed game model is an ordinal potential game with unique Nash equilibrium. Simulations are performed on an energy system with 6 S. E. Hubs, one electricity utility, and one natural gas utility companies. The results confirm that the IDR program can benefit both the customer side, by reducing the electricity and gas consumption cost, and the supplier side, by reducing the peak load demand in the electricity and natural gas load profiles. <s> BIB011 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Intermittent generation is challenging for the existing power systems, which have difficulties in following varying electricity production. To help the generation and demand be in balance, demand response (DR) provides one promising approach. Among many DR applications, residential micro-CHP systems can be advantageous, as these systems typically comprise storage in addition to a controllable cogeneration unit. Current state-of-the-art control strategies for residential micro-CHP systems utilize time-varying electricity price to optimally operate the system. Thus, such systems have the ability to export or import electricity when either one is beneficial. <s> BIB012 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> The paper presents the operational results of a real life residential microgrid which includes six apartments, a 20kWp photovoltaic plant, a solar based thermal energy plant, a geothermal heat pump, a thermal energy storage, in the form of a 1300l water tank and two 5.8kWh batteries supplying, each, a couple of apartments. Thanks to the thermal energy storage, the solar based thermal energy plant is able to satisfy the 100% of the hot water summer demand. Therefore the thermal energy storage represents a fundamental element in the management of the residential demand of thermal energy. It collects renewable thermal energy during day-time to release it during night-time, effectively shaving the peak of the thermal energy demand. The two electric storages, on the other hand, provide the hosted electrical subsystems with the ability to effectively increase the self-consumption of the local energy production, thus lowering the amount of energy surplus to be sold back to the grid, and increasing the self-sufficiency of the microgrid. For instance, the storage has supported self-consumption up to the 58.1% of local energy production with regard to the first battery, and up to the 63.5% with regard to the second one. Also, 3165 and 3365 yearly hours of fully autonomous activity have been recorded thanks to the first, and the second battery respectively. On the other hand, the yearly average efficiency amounts to 63.7%, and 65.3% respectively, for the first and second battery. In the second part of the paper we propose a computational framework to evaluate the overall performance of the microgrid system, while accounting different operating conditions and energy management policies. From this perspective, the framework acts as a useful modeling and design tool, to assess the opportunity of employing alternative energy management system topologies and strategies. Eight different configurations, with growing complexity, have been derived from the original system on purpose. The simulations, carried out based on real data related to one-year time period, have provided results showing that, the higher the integration level of electrical and thermal storage is, the higher degree of self-sufficiency can be achieved by the microgrid, and, in turn, the more consistent the yearly energy saving become. Nevertheless, despite the energy cost reduction achievable with the availability of storage systems in the Leaf House, their high investment cost made them not really profitable at the current price conditions for devices and energy purchase. <s> BIB013 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> In this study we present a Model Predictive Control (MPC) approach to Energy Management Systems (EMSs) for multiple residential microgrids. The EMS is responsible for optimally scheduling end-user smart appliances, heating systems and local generation devices at the residential level, based on end-user preferences, weather-dependent generation and demand forecasts, electric pricing, technical and operative constraints. The core of the proposed framework is a mixed integer linear programming (MILP) model aiming at minimizing the overall costs of each residential microgrid. At each time step, the computed optimal decision is adjusted according to the actual values of weather-dependent local generation and heating requirements; then, corrective actions and their corresponding costs are accounted for in order to cope with imbalances. At the next time step, the optimization problem is re-computed based on updated forecasts and initial conditions. The proposed method is evaluated in a virtual testing environment that integrates accurate simulators of the energy systems forming the residential microgrids, including electric and thermal generation units, energy storage devices and flexible loads. The testing environment also emulates real-word network medium conditions on standard network interfaces. Numerical results show the feasibility and the effectiveness of the proposed approach. <s> BIB014 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> In this paper, we propose a solution to the problem of scheduling of a smart home appliance operation in a given time range. In addition to power-consuming appliances, we adopt a photovoltaic (PV) panel as a power-producing appliance that acts as a micro-grid. An appliance operation is modeled in terms of uninterruptible sequence phases, given in a load demand profile with a goal of minimizing electricity cost fulfilling duration, energy requirement, and user preference constraints. An optimization algorithm, which can provide a schedule for smart home appliance usage, is proposed based on the mixed-integer programming technique. Simulation results demonstrate the utility of our proposed solution for appliance scheduling. We further show that adding a PV system in the home results in the reduction of electricity bills and the export of energy to the national grid in times when solar energy production is more than the demand of the home. <s> BIB015 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Abstract Energy crisis along with environmental concerns are some principal motivations for introducing “energy hubs” by integrating energy production, conversion and storage technologies such as combined cooling, heating and power systems (CCHPs), renewable energy resources (RESs), batteries and thermal energy storages (TESs). In this paper, a residential energy hub model is proposed which receives electricity, natural gas and solar radiation at its input port to supply required electrical, heating and cooling demands at the output port. Augmenting the operational flexibility of the proposed hub in supplying the required demands, an inclusive demand response (DR) program including load shifting, load curtailing and flexible thermal load modeling is employed. A thermal and electrical energy management is developed to optimally schedule major household appliances, production and storage components (i.e. CCHP unit, PHEV and TES). For this purpose, an optimization problem has been formulated and solved for three different case studies with objective function of minimizing total energy cost while considering customer preferences in terms of desired hot water and air temperature. Additionally, a multi-objective optimization is conducted to consider consumer's contribution to CO2, NOx and SOx emissions. The results indicate the impact of incorporating DR program, smart PHEV management and TES on energy cost reduction of proposed energy hub model. <s> BIB016 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Providing a cost-efficient and reliable energy is one of the main issues in human societies of the 21st century. In response to this demand, new features of micro grid technology have provided huge potentials, specifically by the capability of having an interactive coordination between energy suppliers and consumers. Accordingly, this paper offers an improved model for achieving an optimal Demand Response programing. To solve the proposed multi-objective optimization problem, Artificial Bee Colony algorithm and quasi-static technique are utilized. The considered objectives in this paper are minimizing the overall cost of energy consumption and also improving the technical parameters of micro grid over a time horizon. This optimization is subject to several constraints such as satisfying the energy balance and the operating constraints of each energy supply sources. Manageable load or load as source is another enabling feature existing in smart energy networks, which is considered in this paper and its effect on cost reduction and reliability improvement is studied. Trying to examine the performance of the proposed Demand Response Programing in real conditions, the uncertainties are also analyzed by stochastic methods. The results show significant improvements which are obtained by applying just intelligent programming and management. <s> BIB017 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Abstract With the emergence of smart grid which has advanced metering infrastructure (AMI) and microgrid running as a local energy provider for dwellings through distributed energy resources (DERs), dwellers have the opportunity to schedule their in-home energy usage by themselves to reduce energy expense. Household tasks along with DER operation are scheduled according to electricity real-time price (RTP) and natural gas fixed price. Various studies have shown that the lack of effective home automation systems and the lack of awareness among users to respond to time-varying prices are two chief obstacles in order to fully exploit the potential benefits of dynamic pricing schemes. This paper drives to handle these problems by proposing an automatic and optimal residential energy consumption scheduling technique which tries to achieve a favorable trade-off between minimizing the energy costs as well as the inconvenience for the operation of both electrical and thermal in a smart home environment. Simulation results show that the proposed scheduling method leads to significant reduction in energy costs for diverse load scenarios with the electricity demand from the grid. Therefore, the deployment of the proposed method is advantageous for both users and utilities. <s> BIB018 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> The concept of smart grid SG has been introduced to improve the operation of the power systems. In modern structures of power systems, different reasons prompt researchers to suggest integrated analysis of multi-carrier energy systems. Considering synergy effects of the couplings between different energy carriers and utilising intelligent technologies for monitoring and controlling of energy flow may change energy system management in the future. In this paper, we propose a new solution which is entitled ‘smart energy hub’ SEH that models a multi-carrier energy system in a SG. SEH solutions allow homeowners to manage their energy consumption to reduce their electricity and gas bill. We present this concept for a residential customer by an ‘energy management system’ which uses reinforcement learning algorithm and Monte Carlo estimation method for finding a near optimal solution. The simulation results show that by using this concept and then applying the algorithm for a residential customer, running costs are reduced up to 40% while keeping the household owner's desired comfort levels. <s> BIB019 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Abstract Nowadays with the emerging of smart micro-grids(SM-Gs) in residential sectors, a large portion of energy consumption can be saved through optimal scheduling of household devices and management of domestic hybrid energy sources. By the aid of such technologies, residential consumers have the capability to mitigate their energy costs and satisfy their own requirements paying less attention to the configuration of the energy supply system. This paper presents a novel residential energy management system (REMS) to improve the efficiency of energy consumption in a typical SM-G taking into account minimum cost of energy as well as maximum user's comfort level as competitive objectives. The optimization model is also formulated as a mixed integer nonlinear problem (MINLP) and its performance is tested under different operating scenarios with real data. The simulation results show that the proposed model not only reduces energy consumption costs, but also ensures a comfortable lifestyle for occupants. <s> BIB020 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Abstract Residential fuel cell based combined heat and power (FC-CHP) systems have been commercialized and are penetrating the marketed in Japan. In order to utilize FC-CHP systems efficiently, operational strategies are required. Energy interchange among consumers improves efficiency by allowing flexible operation of equipment. An energy management system (EMS), which is installed at the consumer dwelling, can integrate the control and management of energy equipment by using an optimized planning approach that incorporates the energy interchange and can operates equipment whilst adjusting to real conditions. An EMS has been developed, which achieves optimal operation of FC-CHP systems with energy interchange between dwellings in a residential area. Case studies were performed to evaluate the EMS model by using evaluation indices including energy costs, CO2 emissions, and primary energy consumption. The results showed that 1) energy management and energy interchange were effective and contributed to cost reductions, CO2 mitigation, and reduction of primary energy consumption and 2) the developed EMS model achieved optimal operations. <s> BIB021 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> This study deals with optimal resources planning of energy resources in smart home energy system (SHES), where PV (Photovoltaic) panel, battery and electrical heater (EH) are employed to meet all electrical and thermal demands. The advantage of this configuration is avoiding the use of two different energy carriers including electricity and natural gas in downstream users, in addition, reduction of SHES operation cost. An energy management controller (EMC) based on invasive weed optimization (IWO) algorithm is designed in order to optimize the energy routing and scheduling of different resources to obtain an efficient look-up table, by which the power generation of each energy resource is determined for all hourly time intervals. Different electricity tariffs and all operational constraints of energy resources are considered. Several scenarios are defined and the impacts of system components on SHES operation cost are investigated. Moreover, feasibility study of the proposed SHES based on different electricity and gas prices is conducted. It is observed that the proposed optimal scheduling based on IWO algorithm outperforms other approaches. Furthermore, it is shown that self-discharge rate of the battery, affects the system operational performance, especially time of charging and discharging of the battery. <s> BIB022 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Multiple energy carrier systems stem from the need to evolve traditional electricity, gas and other energy systems to more efficient, integrated energy systems. An approach is presented, for controlling multiple energy carriers, including electricity (AC or DC), heat, natural gas and hydrogen, with the objective to minimise the overall cost and/or emissions, while adhering to technical and commercial constraints, such as network limits and market contracts. The technique of multi-agent systems (MAS) was used. The benefits of this approach are discussed and include a reduction of more than 50% in the balancing costs of a potential deviation. An implementation of this methodology is also presented. In order to validate the operation of the developed system, a number of experiments were performed using both software and hardware. The results validated the efficient operation of the developed system, proving its ability to optimise the operation of multiple energy carrier inputs within the context of an energy hub, using a hierarchical multi-agent system control structure. <s> BIB023 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> A smart house generally has a Photovoltaic panel (PV), a Heat Pump (HP), a Solar Collector (SC) and a fixed battery. Since the fixed battery can buy and store inexpensive electricity during the night, the electricity bill can be reduced. However, a large capacity fixed battery is very expensive. Therefore, there is a need to determine the economic capacity of fixed battery. Furthermore, surplus electric power can be sold using a buyback program. By this program, PV can be effectively utilized and contribute to the reduction of the electricity bill. With this in mind, this research proposes a multi-objective optimization, the purpose of which is electric demand control and reduction of the electricity bill in the smart house. In this optimal problem, the Pareto optimal solutions are searched depending on the fixed battery capacity. Additionally, it is shown that consumers can choose what suits them by comparing the Pareto optimal solutions. <s> BIB024 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> PV installations in buildings can utilize different on-site flexibility resources to balance mismatch in electricity production and demand. This paper studies cost-optimal and rule-based control for buildings with PV, employing a heat pump, thermal and electrical storage and shiftable loads as flexibility sources to increase the value of PV for the prosumer. The cost-optimal control minimizes variable electricity cost employing market data on electricity price and optionally constrains grid feed-in to zero; the rule-based control aims at maximizing PV self-consumption. The flexibility strategies are combined into a simulation model to analyze different system configurations over a full year. <s> BIB025 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Pre-packaged cogeneration systems are popular as lead supply in distributed and isolated energy systems aimed at empowering remote off-grid communities and in supporting eco-villages in nature reserves. Energy cooperatives and independent power producers deploy cogeneration systems in community shared microgrid configurations as building blocks in smart localised multi-carrier energy grids. Small-scale renewable energy and distributed energy resources in the energy mix of multi-carrier combined heat and power microgrids call for the development of intelligent control automation solutions. This paper describes a multi-objective optimization solution where hierarchical digital microgrid control is integrated into solar powered micro-cogeneration. The control solution includes an integrated cost-competitive economic and environmental optimization algorithm customized around the needs of small off-grid isolated village settlements. A secondary stage optimization loop functions as a storage capacity decision support system, to alert the community when operating costs can be reduced through an investment into increased storage capacity for the village microgrid. This cost-aware control optimization algorithm is tested in a rural village microgrid by using parametric computer simulation models of a hybrid residential solar cogeneration system. Digital simulation experiments evaluate the operational plans and operational cost performances of energy management for different storage scenarios at an isolated remote rural location in Africa. <s> BIB026 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Abstract Today, as a consequence of the growing installation of efficient technologies, e.g. micro-combined heat and power (micro-CHP), the integration of traditionally separated electricity and natural gas networks has been attracting attentions from researchers in both academia and industry. To model the interaction among electricity and natural gas networks in distribution systems, this paper models a residential customer in a multi-energy system (MES). In this paper, we propose a fully automated energy management system (EMS) based on a reinforcement learning (RL) algorithm to motivate residential customers for participating in demand side management (DSM) programs and reducing the peak load in both electricity and natural gas networks. This proposed EMS estimates the residential customers’ satisfaction function, energy prices, and efficiencies of appliances based on the residential customers’ historical actions. Simulations are performed for the sample model and results depict how much of each energy, i.e. electricity and natural gas, the residential customer should consume and how much of natural gas should be converted in order to meet electricity and heating loads. It is also shown that the proposed RL algorithm reduces residential customer energy bill and electrical peak load up to 20% and 24%, respectively. <s> BIB027 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Abstract The deployment of innovative energy management (EM) approaches based on systematic modelling and optimisation techniques has received an increasing amount of attention in the last decade. This has been often prompted by more stringent energy policy objectives aiming at reducing carbon emissions, phasing out nuclear plants and promoting overall energy efficiency, while containing both capital and operating costs. In this respect the energy hub concept has proven to be a popular approach for operating technologies and units comprising diversified energy carriers, small-scale production units, storage devices and converter systems. Additionally, developments in communication network and control infrastructure afford the possibility, at least in principle, to actively steer and adjust the load on the demand side of the energy balance, leading to the formulation of demand side management (DSM) techniques. This paper proposes an EM solution that combines the features and advantages of both of the aforementioned approaches, i.e. the energy hub framework and DSM methods. The key idea is to combine the supply-side characteristics of energy hubs with the demand side flexibility yielded by the deployment of DSM schemes. This combined approach is validated on an existing building complex by formalizing its energy supply system as an integrated hub and by modelling its heating demand based on thermodynamic principles. Numerical results based on this experimental setup are presented, illustrating that the combined approach can lead to overall savings typically exceeding 10% compared to a baseline scenario where no EM solution is applied, i.e. where only a rule-based heuristic is employed to control the available energy assets, and underscoring the advantages brought by a systematically integrated modelling and optimisation approach. The proposed solution is thus of interest for a broad host of installations in the residential and commercial domain, and for the latter a specific real-world example has been explicitly considered and analysed. The obtained results are encouraging and warrant further analysis and investigation. <s> BIB028 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> This work describes an implementation of an office and/or personal smart grid for environmentally friendly buildings. These can be equipped with a local energy source (e.g., photovoltaic panels or combined heat-power units), energy storage devices (batteries, electric hot water boilers, heating, and ventilation systems including air conditioning), a building energy management system with sensors (e.g., providing the room temperatures), and household appliances acting as actuators (in general, split into groups of schedulable and non-schedulable ones). The idea behind this work has been to develop an automatic control system which would optimally decide for the end customer when to buy, sell, or store electric energy with the objective to minimize his total costs. At the same time, it fulfills all constraints in terms of the limits on power allowed to be taken from the grid. For the user, besides the optimal scheduling of household appliances and selling energy to the grid whenever it becomes profitable, the developed prototype gives also the opportunity to monitor the local generation, storage, and consumption in real time. On the other side, the electric utility can produce a real-time pricing signal which reflects daily peaks in consumption. Making such a price signal available for this building energy management system will lead to a decrease of power consumption from the grid. In this project, a model predictive control approach to the energy optimization problem in a building has been proposed. Different scenarios have been run and the results are discussed here. <s> BIB029 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> In a multi-energy system, there are different types of dependencies among the energy carriers. Internal dependencies refer to possible changes in the energy source in the presence of energy converters and storage and are managed by the system operator through the control strategies applied to the equipment. External dependencies are due to the choice of the energy supply according to customer preferences, when alternative solutions are available. This paper introduces a new model of the external dependencies within a multi-generation representation based on energy hubs. The external dependencies are addressed through a stochastic model, in order to take into account the possible uncertainty in the customers' decisions. This model is then used to introduce carrier-based demand response, in which the user participates in demand response programmes aimed at promoting the shifting among different energy sources by preserving the service provided to the end users. The results obtained from the new model in deterministic and stochastic cases indicate the appropriateness and usefulness of the proposed approach. <s> BIB030 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Abstract Today, as a consequence of the growing installation of efficient technologies, such as combined heat and power (CHP) as a co-generation, the integration of electricity through grid supply, Photovoltaic (PV) and energy storage systems as an integrated network is attracting a lot of attention in smart grid applications. To model the interaction among electricity and natural gas, the energy hub framework is adopted to determine a modelling procedure for such multi-carrier energy systems. This paper presents a residential energy hub model for a smart home as a modified framework of conventional energy hubs in a smart grid with consideration of heat pump water heater, coordination of sources and carbon emissions. Therefore, this study is twofold; the first part optimizes the operation of the combined CHP, Photovoltaic and storage system under dynamic pricing. Since residential load management plays a key role in realizing household demand response programs in a smart grid, performing optimal load management in the proposed residential energy hub model is also studied in this paper. To achieve this, the optimization problem is extended by considering modelling of a heat pump water heater. It is also found out that CO 2 signal could give customers an environmental motivation to shift or reduce loads during peak hours, as it would enable co-optimization of electricity consumption costs and carbon emissions reductions. <s> BIB031 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> This paper introduces a management model for optimal scheduling of a multi-carrier energy hub. In the proposed hub, three types of assets are considered: dispersed generating systems (DGs) such as micro-combined heat and power (mCHP) units, storage devices such as battery-based electrical storage systems (ESSs), and heating/cooling devices such as electrical heater, heat-pumps and absorption chillers. The optimal scheduling and management of the examined energy hub assets in line with electrical transactions with distribution network is modeled as a mixed-integer non-linear optimization problem. In this regard, optimal operating points of DG units as well as ESSs are calculated based on a cost-effective strategy. Degradation cost of ESSs is also taken into consideration for short-term scheduling. Simulation results demonstrate that including well-planned energy storage options together with optimal scheduling of generating units can improve the economic operation of the multi-carrier energy hub while meeting the system's constraints. <s> BIB032 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Abstract With the increasing penetration of distributed renewable energy generation and dynamic electricity pricing schemes, applications for residential demand side management are becoming more appealing. In this work, we present an optimal control model for studying the economic and grid interaction benefits of smart charging of electric vehicles (EV), vehicle-to-grid, and space heating load control for residential houses with on-site photovoltaics (PV). A case study is conducted on 1–10 net zero energy houses with detailed empirical data, resulting in 8–33% yearly electricity cost savings per household with various electric vehicle and space heating system combinations. The self-consumption of PV is also significantly increased. Additional benefits through increasing the number of cooperating households are minor and saturate already at around 3–5 households. Permitting electricity transfer between the houses and EV charging stations at workplaces increases self-sufficiency significantly, but it provides limited economic benefit. The additional cost savings from vehicle-to-grid compared to smart charging are minor due to increased battery degradation, despite a significant self-sufficiency increase. If the optimization is conducted without taking the battery degradation cost into account, the added monetary value of vehicle-to-grid can even be negative due to the unmanaged degradation. Neglecting battery degradation completely leads to overestimation of the vehicle-to-grid cost benefit. <s> BIB033 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> This paper aims to optimize total energy costs in an operational model of a novel energy hub (EH) in a residential area. The optimization problem is set up based on daily load demand (such as electricity, heat, and cooling) and time-of-use (TOU) energy prices. The extended EH model considers the involvement of solar photovoltaic (PV) generation, solar heat exchanger (SHE), and a battery energy storage system (BESS). A mathematical model is constructed with the objective of optimizing total energy cost during the day, including some constraints such as input-output energy balance of the EH, electricity price, capacity limitation of the system, and charge/discharge power of BESS. Four operational cases based on different EH structures are compared to assess the effect of solar energy applications and BESS on the operational efficiency. The results show that the proposed model predicts significant changes to the characteristics of electricity and gas power bought from utilities, leading to reduced total energy cost compared to other cases. They also indicate that the model is appropriate for the characteristics of residential loads. <s> BIB034 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Abstract Based on the kind of fuel consumed by generation unit, each generation system has different generation costs and emits various types of greenhouse gases like CO 2 , SO 2 and NO 2 to the atmosphere. So, nowadays in the power system scheduling, emission issue has been turned to be an important factor. In this paper, in addition to economic performance, emission problem of energy hub system has been also investigated. Therefore, a multi-objective optimization model has been proposed for cost-environmental operation of energy hub system in the presence of demand response program (DRP). Weighted sum approach has been employed to solve the proposed multi-objective model and fuzzy satisfying technique has been implemented to select the best compromise solution. Implementation of load management programs presented by DRP shifts some percentage of load from peak periods to off-peak periods to flatten load curve which leads to reduction of total cost and emission of energy hub system. A mixed-integer linear programming has been used to model the cost-environmental performance problem of energy hub system and then, GAMS optimization software has been utilized to solve it. A sample energy hub system has been studied and the obtained results have been compared to validate the effectiveness of proposed techniques. <s> BIB035 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Abstract Optimal operation of hybrid energy systems is a big challenge in power systems. Nowadays, in addition to the optimum performance of energy systems, their pollution issue has been a hot topic between researchers. In this paper, a multi-objective model is proposed for economic and environmental operation of a battery/fuel cell/photovoltaic (PV) hybrid energy system in the presence of demand response program (DRP). In the proposed paper, the first objective function is minimization of total cost of hybrid energy system. The second objective function is minimization of total CO 2 emission which is in conflict with the first objective function. So, a multi-objective optimization model is presented to model the hybrid system’s optimal and environmental performance problem with considering DRP. The proposed multi-objective model is solved by e-constraint method and then fuzzy satisfying technique is employed to select the best possible solution. Also, positive effects of DRP on the economic and environmental performance of hybrid system are analyzed. A mixed-integer linear program is used to simulate the proposed model and the obtained results are compared with weighted sum approach to show the effectiveness of proposed method. <s> BIB036 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Renewable energy resources are often known as cost-effective and lucrative resources and have been widely developed due to environmental-economic issues. Renewable energy utilization even in small scale (e.g., microgrid networks) has attracted significant attention. Energy management in microgrid can be carried out based on the generating side management or demand side management. In this paper, portable renewable energy resource are modeled and included in microgrid energy management as a demand response option. Utilizing such resources could supply the load when microgrid cannot serve the demand. This paper addresses energy management and scheduling in microgrid including thermal and electrical loads, renewable energy sources (solar and wind), CHP, conventional energy sources (boiler and micro turbine), energy storage systems (thermal and electrical ones), and portable renewable energy resource (PRER). Operational cost of microgrid and air pollution are considered as objective functions. Uncertainties related to the parameters are incorporated to make a stochastic programming. The proposed problem is expressed as a constrained, multi-objective, linear, and mixed-integer programing. Augmented Epsilon-constraint method is used to solve the problem. Final results and calculations are achieved using GAMS24.1.3/CPLEX12.5.1. Simulation results demonstrate the viability and effectiveness of the proposed method in microgrid energy management. <s> BIB037 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> As the installed capacity of wind power is growing, the stochastic variability of wind power leads to the mismatch of demand and generated power. Employing the regulating capability of demand to improve the utilization of wind power has become a new research direction. Meanwhile, the micro combined heat and power (micro-CHP) allows residential consumers to choose whether generating electricity by themselves or purchasing from the utility company, which forms a residential hybrid energy system. However, the impact of the demand response with hybrid energy system contained micro-CHP on the large-scale wind power utilization has not been analyzed quantitatively. This paper proposes an operation optimization model of the residential hybrid energy system based on price response, integrating micro-CHP and smart appliances intelligently. Moreover, a novel load aggregation method is adopted to centralize scattered response capability of residential load. At the power grid level, a day-ahead stochastic economic dispatch model considering demand response and wind power is constructed. Furthermore, simulation is conducted respectively on the modified 6-bus system and IEEE 118-bus system. The results show that with the method proposed, the wind power curtailment of the system decreases by 78% in 6-bus system. In the meantime, the energy costs of residential consumers and the operating costs of the power system reduced by 10.7% and 11.7% in 118-bus system, respectively. <s> BIB038 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Abstract Demand Side Management (DSM) provides ancillary service to the electric grid by modifying customers electricity demand. Demand Response (DR) and Demand Flexibility (DF) programs from buildings are well-adopted ancillary services to reduce the peak demand in grids by altering the power consumption strategy. Heating, Ventilation and Air-Conditioning (HVAC) systems are one of the largest energy demands in commercial buildings. In addition, HVAC systems are flexible to provide DR service to the grid. In this study, two common configuration topologies of building integration with Energy Storage Systems (ESS) and renewables are considered. A real-time optimization framework based on Model Predictive Control (MPC) is designed to control the power flow from the grid, solar Photovoltaic (PV) panels, and ESS to a commercial building with HVAC systems. The MPC framework uses the inherent thermal mass storage of the building and the ESS as a means to provide DR. Deterministic and probabilistic analysis are studied to investigate the effectiveness of the proposed framework on Building-to-Grid (B2G) systems. Our deterministic results show that the proposed optimization and control framework for B2G systems can significantly reduce the maximum load ramp-rate of the electric grid to prevent duck-curve problems associated with increase in solar PV penetration into the grid. Based on probabilistic results, even under prediction uncertainties, electricity cost saving and ramp-rate reduction is achievable. The results show that this DR service does not affect the building indoor climate in a way noticeable to humans and its effect on the operational building costs is reduced. The B2G simulation testbed in this paper is based on the experimental data obtained from an office building, PV panels, and battery packs integrated with a three-phase unbalanced distribution test feeder. A Monte-Carlo simulation is carried out to account for uncertainties of the proposed method. Both deterministic and stochastic analyses show the effectiveness of the proposed predictive power flow control to decrease the building operation electricity costs and load ramp-rates. <s> BIB039 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Due to the 2012 change in the renewable energy act the feed-in tariffs, and therefore the number of newly installed photovoltaic systems decreased dramatically in Germany. Particularly in the residential sector as the biggest market new business ideas for photovoltaic systems were developed. Hence a photovoltaic and combined heat-and-power system, which provides not only electricity but also heat. This complex system requires flexible control strategies. A new combined control algorithm is proposed that in contrast to the standard strategies can operate even under incorrect weather and load forecasts without creating discomfort. Furthermore is it applicable for cloud-based solutions. In this paper a high-level model predictive control based on a mixed integer linear programming problem is combined with an additional low-level, rule-based controller. The low-level control compares the set-points of the optimization with the actual values and corrects the set-points according to each system component until the next optimization takes place. The results show that a hybrid system can be successfully controlled by a combined control approach. In case of a cloud-based application the need for an optimization can be reduced by a factor of four without diminishing comfort or facing much higher operational costs. It has also been shown that the combined control algorithm can be used as an energy management of microgrids. <s> BIB040 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Abstract The Energy Hub has become an important concept for formally optimizing multi-carrier energy infrastructure to increase system flexibility and efficiency. The existence of energy storage within energy hubs enables the dynamic coordination of energy supply and demand against varying energy tariffs and local renewable generation to save energy cost. The battery lifetime cost may be included in the optimization objective function to better utilize battery for long term use. However, the operational optimization of an interconnected energy hub system with battery lifetime considered presents a highly constrained, multi-period, non-convex problem. This paper proposes Particle Swarm Optimization (PSO) hybridised with a numerical method, referred to collectively as the decomposition technique. It decouples the complicated optimization problem into sub-problems, namely the scheduling of storage and other elements in the energy hub system, and separately solves these by PSO and the numerical method ‘interior-point’. This approach thus overcomes the disadvantages of numerical methods and artificial intelligence algorithms that suffer from convergence only to a local minimum or prohibitive computation times, respectively. The new approach is applied to an example two-hub system and a three-hub system over a time horizon of 24 h. It is also applied to a large eleven-hub system to test the performance of the approach and discuss the potential applications. The results demonstrate that the method is capable of achieving very near the global minimum, verified by an analytical approach, and is fast enough to allow an online, receding time horizon implementation. <s> BIB041 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> In this paper, an optimal probabilistic scheduling model of energy hubs operations is presented. The scheduling of energy hub determines the energy carriers to be purchased as input and converted or stored, in order to meet the energy requests, while minimizing the total hub's cost. However, as many other engineering endeavors, future operating criteria of energy hubs could not be forecasted with certainty. Load and price uncertainties are the most unclear parameters that hub operators deal with. In this regard, this paper proposes a 2 $m$ + 1 point estimation probabilistic scheduling scheme for energy hubs with multiple energy inputs and outputs. One of the applicable tools of energy hubs to have an efficient participation in the liberalized power market with volatile prices is demand response programs (DRPs). While there is plenty of experience in investigating the effect of DRP, it is electricity DRP that receives increasing attention by research and industry. Therefore, the proposed DRP investigates the effect of both responsive thermal and electric loads in reducing the total cost and participation of different facilities in supplying multiple loads. The proposed model envisages the most technical constraints of converters and storages. Several test systems have been investigated in order to confirm the effectiveness of the proposed model. The results verify the capability of the proposed model in covering the energy hub time-varying output demands as well as the economic advantages of implementing the suggested strategy. In addition, the results are compared with 2 $m$ point estimate method and Monte Carlo simulation. <s> BIB042 </s> Energy management for user’s thermal and power needs: A survey <s> Appendix B. Economic and environmental savings <s> Abstract This paper proposes a new optimization model for Smart Home Management Systems (SHMS) in order to increase the profits of Load Serve Entities (LSEs) and customers from technical and financial points of view. In the recent decades, performing Demand Response (DR) is one of the most efficient ways to improve the performance of power distribution systems in terms of power loss, and investment costs. The LSEs can implements some strategies like offering incentives to customers to change their consumption pattern with the aim of reducing power loss, improving asset management and increasing the profits. On the other hand, the end users can participate in DR programs to decrease electricity bills and earn monetary incentives from LSEs proportionate to their contributions to the energy loss reduction. In this paper, Distribution Locational Marginal Price (DLMP) instead of time-based pricing mechanism is applied to bill the customers. In the proposed strategy, the energy bill of customers and power loss of the system are simultaneously decreased. For dealing with uncertainties, stochastic variables computation module is designed which generates several scenarios by Monte Carlo simulation at each hour. The operation of household resources and appliances are optimized through a Mixed Integer Linear Programming (MILP), which has a two-stage stochastic model. The results explicitly show benefits of the proposed stochastic model since it provides accuracy in scheduling and decreases the operation cost. Besides, the superiority of DLMP and the proposed method over existing pricing mechanism is demonstrated. <s> BIB043
Traditional energy systems are managed such that the supply of energy follows the demand, having a (limited) flexibility on the generation side, provided by spinning reserve and balancing power of centralized plants. In this scenario, any generation or consumption mismatch has to be compensated by other generating units, either already running or that can be rapidly turned on or off. Such balancing service has to be remunerated and its costs affects the total cost of the power system, up to 5% BIB008 . The use of (almost) real-time data at the distribution level can have a significant impact on both economic and environmental costs, making the system more flexible and ready to balance RES and load uncertainties, reducing the import of power from the main grid and improving the overall efficiency. In particular, it is estimated that by using short-term predictions of the thermal demand and RES production, 2% to 6% of daily energy costs and emissions of a MG can be saved BIB009 BIB010 , compared to a fully known scenario. When including also the power demand, energy costs and emissions can be reduced by 10%-20% and by 5%-10%, respectively, compared to simulations without demand prediction BIB021 . According to Moghaddas Tafreshi et al. (2016), BIB003 and BIB040 , ignoring uncertainties of energy systems such as load and RES forecasts could misrepresent their real world operation and economic aspects, overestimating the potential system savings by 0.1% to 7%. The development of hybrid systems is encouraged by looking at the energy demand as a whole, including both power and heat, which are supplied by multiple energy carriers, usually electricity and hot water. In this context, CHPs play a central role, with the possibility of simultaneously producing electricity and heat, by burning natural gas or other fuels. The coordinated operation of multiple energy carriers decrease the overall operation costs and improve the energy efficiency of the system , taking advantage of the dispatchable nature of fossil-fired components, unless the costs for the infrastructure and distribution of fossil fuels are not affordable BIB022 . In a traditional scenario, a boiler usually supplies the thermal load, while the grid supplies the electricity for the power demand, as well as the cooling one by means of a compression chiller. Assuming such scenario as a reference, Brandoni et al. show that a microCHP can yield up to 12% and 6% of energy savings in a residential 10flat apartment building and in an office, respectively BIB007 . At the same time, energy bills could be cut by 8% and 9%, respectively. Further improvements can be achieved by employing a hybrid microCCHP and a solar unit, reaching 29% and 33% energy saving, and 45% and 67% cost saving in the residential building and the office, respectively. Compared to a traditional scenario without coupling of different energy carriers, a MG with 20 to 90 households can reduce daily energy costs by 6% BIB023 to 17% BIB004 , and, similarly, by 15% for a 10-apartment building  . More promising economic savings of around 30%-35%, are estimated for a 750-customer community in BIB011 and for a four-houses system in  , while a CHP can enable up to 23% cost savings in a single smart home BIB031 . Moreover, a microCHP-based system can take advantage of low electricity price hours and deal with electricity surplus by including a resistor into the heating system, increasing the economic savings by 1.5% BIB012 . Other key elements for the coupling of electricity and natural gas are the storage devices. A TES stores heat produced by gas, but also electricity heat, for instance via an electric resistance. Similarly, a battery can be charged by the electricity generated by a gas-burning CHP as a by-product. The optimal size of both electrical and thermal storage devices is a critical aspect of the energy management problem as, on the one hand, an under-sized device can limit the potential benefits, but on the other hand, an over-sized one can cause great losses BIB012 . The effects of battery's capacity in terms of electricity bill and costs reduction due to load regulation are discussed in BIB024 . The study shows somehow intuitively that a large battery capacity reduces electricity bills, as more energy can be stored during low price hours, and, at the same time, minimizes the required load regulation. However, technological limits and current investment costs can represent an obstacle to the employment of devices with larger capacity BIB013 . In particular, battery degradation costs can penalized the operation costs of the overall system by around 1% BIB032 BIB041 , making it necessary to limit the discharging to 60%-90% of storage capacity BIB041 and, hence, the potential savings. According to BIB033 , neglecting the battery degradation can lead to overestimate the cost-benefit of vehicle to grid (V2G) technologies up to 5%, especially when new batteries are considered and their degradation is faster. As for the storage efficiency, the results in BIB022 indicates that electricity prices influence the lowest efficiency of battery that allows it to cooperate in shifting energy over time and reducing the energy bills. If the storage efficiency is lower than the threshold, the battery will fully discharge in the first interval of peak price and its operation will not be economically convenient. Nevertheless, the potential benefits enabled by the use of storage are indisputable. In a favorable solar day conditions, using solar panels coupled with a storage system can reduce the daily energy costs of an energy hub supplying residential loads by around −15% BIB034 BIB041 BIB031 , and up to 25% for four smart homes BIB022 . For residential MGs, a storage is estimated to allow 3%-6% BIB001 , 11% BIB014 and 23% BIB009 of cost savings. Introducing an optimalsized storage can reduce the energy costs of a residential building by 19% BIB025 to 54% BIB002 and for a MG by −66% BIB026 . In particular, the integration of electrical and thermal storage with the possibility of using the electricity surplus to produce hot water and supply the heat pump can halve the electricity bought from the grid and reduce by energy costs by up to 45% with respect to a scenario without storage devices BIB013 , improving the self-sufficiency and efficiency of a hybrid system. The analysis presented in BIB005 offers interesting conclusions on the potential benefits of coordinating a group of loads and generation units. The authors show that the use of PV panels and CHPs increase system autonomy from the main distribution grid when prosumers are interconnected, so that they can exchange energy among themselves. By doing so, the energy autonomy can vary between 38%, when PV panels are installed, and 64% with CHPs, considering in both cases that homes are connected, but not centrally coordinated. A further increase on autonomy is reached with an EMS that can control all generation units, reaching 45% with PV panels and up to almost 100% with CHPs and PV panels. Beyond the numerical results, the study gives valuable insights on the importance of interconnection and coordination among different entities in order to enhance potential benefits and make costly solutions more profitable. Together with self-generation and storage of energy, changing the consumption patterns according to DRPs has a great economic and environmental impact, while increasing the flexibility of the overall system in the supply-demand balance . The quantification of the savings significantly varies among the reviewed studies. According to the lowest estimates BIB035 BIB036  , applying DRPs reduces operation costs and total emissions by 0.69%-3.1% and 0.54%-0.84%, respectively. Meanwhile, other studies are more encouraging reaching cost savings up to 7% , 11% BIB037 or 20% BIB027 BIB004 and emission reduction up to 1.5% BIB037 . When DRPs are applied to both thermal and power demands, with the former being curtailable and/or shiftable within a certain temperature bandwidth, system costs can be reduced from 6% BIB042 BIB001 to 10% BIB028 , and up to 15% BIB029 BIB033 . According to BIB031 , flexible appliances alone can reduce energy costs by 47%, and up to 70% when a PV/battery systems and a CHP are used as well. Compared to a non-optimized energy management, the optimal scheduling of residential electric appliances, thermal load, and storage enables between 30% and 65% cost savings within different dynamic price schemes BIB006 BIB003 . DRPs can be further extended to include the management of smart electric vehicles, which act as active loads that are charged during offpeak hours and sell the stored energy at a later high-price time. In this case, the daily savings in energy costs reach up to 20%, compared to a traditional case without DRPs, and even up to 43%, when also smart management of thermal storage and V2G devices are included. On a yearly basis, V2G technology and smart control of space heating achieve up to 20% cost savings for a single household, and up to 33% for a group of 1-10 households, which cooperate and exchange electricity. According to BIB015 , a residential time-of-use-based DRP involving smart appliances, electric vehicle, and AC system can enable a negative net cost of energy when combined with the export of electricity locally produced by a PV panel. Flexible appliances can also promote self-consumption, decreasing grid feed-in by up to 11% BIB025 , which can be advantageous when feed-in prices are significantly lower than market prices. Encouraging results are achieved also in terms of emissions by reducing the import of electricity and promote the use of natural gas in a CHP system, whose emission factor is usually lower than the one of the reference grid, e.g., 2.5 (Skarvelis-Kazakos et al., 2016) to 4.5 BIB016 times lower. When the EMS aims at minimizing the emissions, pollution can be reduced by up to 45%, but the revenues from energy arbitrage (i.e., purchasing and storing energy during off-peak prices periods, and then selling it when prices are the highest) would be limited by the lower import of electricity BIB016 . Emissions minimization can be considered a separate goal from the economic one BIB023 , and, to some extent, also counterpose BIB016 . According to BIB023 , coupling the use of electricity and gas in a 20-household MG allows up to 14% emission reduction. Furthermore, the use of low-carbon technologies can be promoted by incentive schemes, such as feed-in tariffs and carbon tax  . In particular, using an EMS to coordinate the operation of a microCHP-based system can achieve up to 24% annual costs saving when a carbon tax of 570 e/tonCO 2 is applied  . The potentials of DRPs can be further enhanced by installing hybrid appliances  or considering hybrid loads BIB030 , whose required energy can be produced from different energy carriers. The energy efficiency and the flexibility of MESs would be significantly increased. Smart residential building equipped with deferrable hybrid appliances and PV panels can reach up to 60% of self-sufficiency, which is further improved up to almost 100% by introducing a battery  . Such appliances not only can be shifted in time, but they also take advantage of the different costs and emissions tied to multiple energy carriers, reducing the economic  and environmental costs of energy consumptions. Although using electricity for satisfying both the power and thermal demand is usually the most energy-efficient solution thanks to low losses, natural gas is, in fact, often cheaper  BIB030 as well as lower in carbon emission than electricity. The benefits of DRPs are not limited to the user's side. Employing the demand response capability of consumers can significantly reduce the operating costs of the transmission and distribution energy systems. For instance, preventing peaks in the demand promotes a more efficient use of the infrastructure, avoiding bottlenecks, and postponing investments in capacity expansion. In BIB017 , reduction of 11% peak demand is achieved by shifting flexible demand, estimated at 10% of the total consumptions. Thanks to a centralized control of thermal loads and smart appliances, the peak load demand is reduced by approximately 30% when considering around 750 residential , by 5%-25% up to 89% BIB004 for a community with 30 to 90 households, and between 6% and 25% BIB015 BIB027 BIB018 BIB016 for a single household. The work presented in BIB019 and BIB031 shows that by controlling household loads and generation DERs, the peak load are reduced by 17% and up to 70%, respectively, while energy costs and emission costs are decreased by 40%-70% and 50%-80% respectively. In BIB043 , the impact of a nodal-price-based DRP on grid power losses and distribution transformers management is investigated. While the proposed DRP enables the consumer to save around 5 euro per day, when compared to a classical time-of-use DRP; the utility reduces the total energy loss of the grid by 400 kWh a day, and a better management of the demand contributes to increase the lifetime expectation of low voltage distribution transformer. Additionally, DRPs can increase the system's hosting capacity of renewables, avoiding expensive rescheduling procedures of thermal power plants. Assuming that electricity prices decrease with higher share of wind power, users involved in price-based DRPs may be motivated to increase the quantity of power purchased from the utility company and reduce the use of gas for self production. As a result, a win-win situation is achieved. Consumers' energy costs could decrease up to −12%, wind curtailment would be reduced of up to −80%, and the operating costs of the transmission system would drop by 10% BIB038 . Combining a building EMS with a distribution grid controller can enable up to 42% electricity cost savings, while reducing the fast load variation up to −70%, helping the distribution grid operation BIB039 . One possible obstacle to the acceptance of DRPs by users may be a perceived or actual drop in comfort, as appliances are automatically scheduled. This issue can be addressed by giving an economic value to user's discomfort BIB020 BIB001 BIB019 . The discomfort can be related to the electric load, when loads are scheduled and run outside a certain desirable time window set by the user BIB020 BIB019 , and to the PEVs if they are not enough charged when the users need them BIB019 . The user's dissatisfaction is also related to the thermal load, when the difference between the indoor temperature and the set point is greater than a range of a couple of degrees BIB001 BIB020 . The studies show that a smart EMS can reduce the energy costs while guaranteeing a certain level of comfort. In particular, considering a real-time price scheme and a timeof-use one, the smart EMS in Anvari- BIB020 reduces the total operation costs by up to 54% and to 47%, respectively, compared to the use of a purely price-based EMS that does not take user's preferences into account. Constant preferred temperatures for hot water and indoor temperature are shown in BIB018 to increase energy costs by up to 27% when compared to a scenario with a range of values. In particular, energy costs increase by 0.1%-1.8% when constant preferred temperatures increase by 1 • C BIB018 .