Energy management for user’s thermal and power needs: A survey <s> Formulation <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> Formulation <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> Formulation <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> Formulation <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> Formulation <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> BIB005 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <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> BIB006 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <s> Micro-grid technology provides an opportunity and a desirable infrastructure for improving efficiency of electrical and thermal energy consumption. The key to improve electrical and thermal energy efficiency in operation is to coordinate and optimize the operation of various energy resources and loads. In this paper, a typical micro-grid consisting of photovoltaic and wind turbine as renewable resources and diesel generator, combined heat and power and boiler as other Supply resources and battery as system's storage is considered. The objective function was minimizing overall cost of electrical and thermal energy during 24 hours. With regards to demand and supply balance and operation constraints of every energy source and storage and also considering demand response plans, the objective function was minimized by specifying each unit's production or storage. Simulation results show that, a significant save of money and fuel can be achieved through scheduling and control of various energy supply and storage resources. <s> BIB007 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <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> BIB008 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <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> BIB009 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <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> BIB010 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <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> Formulation <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> BIB012 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <s> Microgrids (MGs) are considered as a key solution for integrating renewable and distributed energy resources, combined heat and power (CHP) systems, as well as distributed energy-storage systems. This paper presents a stochastic programming framework for conducting optimal 24-h scheduling of CHP-based MGs consisting of wind turbine, fuel cell, boiler, a typical power-only unit, and energy storage devices. The objective of scheduling is to find the optimal set points of energy resources for profit maximization considering demand response programs and uncertainties. The impact of the wind speed, market, and MG load uncertainties on the MG scheduling problem is characterized through a stochastic programming formulation. This paper studies three cases to confirm the performance of the proposed model. The effect of CHP-based MG scheduling in the islanded and grid-connected modes, as well as the effectiveness of applying the proposed DR program is investigated in the case studies. <s> BIB013 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <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> BIB014 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <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> BIB015 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <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> BIB016 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <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> BIB017 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <s> To enable the efficient utilization of energy carriers and the successful integration of renewable energies into energy systems, building energy management systems (BEMS) are inevitable. In this article, we present a modular BEMS and its customizable architecture that enable a flexible approach towards the optimization of building operation. The system is capable of handling the energy flows in the building and across all energy carriers as well as the interdependencies between devices, while keeping a unitized approach towards devices and the optimization of their operation. Evaluations in realistic scenarios show the ability of the BEMS to increase energy efficiency, self-consumption, and self-sufficiency as well as to reduce energy consumption and costs by an improved scheduling of the devices that considers all energy carriers in buildings as well as their interdependencies. <s> BIB018 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <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> BIB019 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <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> BIB020 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <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> BIB021 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <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> BIB022 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <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> BIB023 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <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> BIB024 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <s> With the development of home area network, residents have the opportunity to schedule their power usage at the home by themselves aiming at reducing electricity expenses. Moreover, as renewable energy sources are deployed in home, an energy management system needs to consider both energy consumption and generation simultaneously to minimize the energy cost. In this paper, a smart home energy management model has been presented that considers both energy consumption and generation simultaneously. The proposed model arranges the household electrical and thermal appliances for operation such that the monetary expense of a customer is minimized based on the time-varying pricing model. In this model, the home gateway receives the electricity price information as well as the resident desired options in order to efficiently schedule the appliances and shave the peak as well. The scheduling approach is tested on a typical home including variety of home appliances, a small wind turbine, PV panel and a battery over a 24-h period. <s> BIB025 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <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> BIB026 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <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> BIB027 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <s> Abstract The energy security and environmental problems impel people to explore a more efficient, environment friendly and economical energy utilization pattern. In this paper, the coordinated operation and optimal dispatch strategies for multiple energy system are studied at the whole Micro Energy Grid level. To augment the operation flexibility of energy hub, the innovation sub-energy hub structure including power hub, heating hub and cooling hub is put forward. Basing on it, a generic energy hub architecture integrating renewable energy, combined cooling heating and power, and energy storage devices is developed. Moreover, a generic modeling method for the energy flow of micro energy grid is proposed. To minimize the daily operation cost, a day-ahead dynamic optimal operation model is formulated as a mixed integer linear programming optimization problem with considering the demand response. Case studies are undertaken on a community Micro Energy Grid in four different scenarios on a typical summer day and the roles of renewable energy, energy storage devices and demand response are discussed separately. Numerical simulation results indicate that the proposed energy flow modeling and optimal operation method are universal and effective over the entire energy dispatching horizon. <s> BIB028 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <s> Renewable energy generation and electric vehicles (EVs) have attracted much attention in the past decade due to increasingly serious environmental problems as well as less and less fossil energy reserves. Moreover, the forms of energy utilization are changing with the development of information technology and energy technology. The term “energy hub” has been introduced to represent an entity with the capability of energy production, conversion and storage. A residential quarter energy-hub-optimization model including a concentrating solar power (CSP) unit is proposed in this work, with solar energy and electricity as its inputs to supply thermal and electrical demands, and the operating objective is to minimize the involved operation costs. The optimization model is a mixed integer linear programming (MILP) problem. Demand side management (DSM) is next implemented by modeling shiftable electrical loads such as EVs and washers, as well as flexible thermal loads such as hot water. Finally, the developed optimization model is solved with the commercial CPLEX solver based on the YALMIP/MATLAB toolbox, and sample examples are provided for demonstrating the features of the proposed method. <s> BIB029 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <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> BIB030 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <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> BIB031 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <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> BIB032 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <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> BIB033 </s> Energy management for user’s thermal and power needs: A survey <s> Formulation <s> There is increasing interest in multi-energy communities, which could become important sources of demand response flexibility, especially when equipped with storage. Their location on distribution networks mean their exploitation to solve local capacity congestions may be particularly valuable, whilst their ability to partake in energy/reserve markets can improve their business cases. However, maximizing this flexibility potential by providing multiple services that are subject to uncertain calls is a challenging modeling task. To address this, we present a stochastic energy/reserve mixed integer linear program for a community energy system with consideration of local network constraints. By covering all the relevant energy vectors, the multi-energy formulation allows comprehensive modeling of different flexibility options, namely, multi-energy storage, energy vector substitution, end-service curtailment, and power factor manipulation. A key feature of the approach is its robustness against any reserve call, ensuring that occupant thermal comfort cannot be degraded beyond agreed limits in the event of a call. The approach is demonstrated through a case study that illustrates how the different flexibility options can be used to integrate more electric heat pumps into a capacity constrained smart district that is managed as a community energy system, while maximizing its revenues from multiple markets/services. <s> BIB034
Nature Works # of works Single-objective Eco BIB015 , BIB008 ,  , BIB016 ,  , BIB034 , BIB023 , BIB030 , BIB017 , BIB018 ,  , BIB031 , BIB009 , BIB019 , BIB024 , BIB003 , BIB020 , BIB025 , BIB004 BIB005 , BIB032 , BIB021 , BIB026 , BIB033 , BIB027 , BIB028 , BIB022 , BIB029 , BIB010 , BIB011 Rouholamini and Mohammadian (2015), BIB012 , BIB013 , BIB001 , BIB014 ,  , BIB006 , BIB007 Soc BIB001 , BIB034 penalty is part of the system operation costs in BIB033 and BIB027 , in order to prevent storage degradation and ensure a longer lifetime. In BIB010 , the total costs of a residential energy hub include user's bills, discomfort costs due to appliances delay and plug-in electric vehicles (PEVs) storage level, and CO 2 emission costs. Dagdougui et al. propose an energy management model for green buildings based on a purely technical optimization problem, whose main objectives are the minimization of imported electricity from the distribution grid and of the deviation from the original demand, and the maximization of stored energy BIB002 . A gametheory based approach is taken in BIB011 , where the payoff function of each prosumer to be maximized includes energy costs, user's satisfaction, and, at the same time, guarantees the maximization of the electricity utility profit.