Energy management for user’s thermal and power needs: A survey <s> DRP <s> This paper presents an overview of demand response (DR) in electricity market. The definition and a classification of demand response will be presented. Different potential benefits as well as cost components of demand response will be presented. The most common indices used for demand response evaluation are highlighted. Moreover, some utilities experiences with different demand response programs will be presented. <s> BIB001 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> BIB002 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> BIB003 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> BIB004 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> DRP <s> The smart grid is conceived of as an electric grid that can deliver electricity in a controlled, smart way from points of generation to active consumers. Demand response (DR), by promoting the interaction and responsiveness of the customers, may offer a broad range of potential benefits on system operation and expansion and on market efficiency. Moreover, by improving the reliability of the power system and, in the long term, lowering peak demand, DR reduces overall plant and capital cost investments and postpones the need for network upgrades. In this paper a survey of DR potentials and benefits in smart grids is presented. Innovative enabling technologies and systems, such as smart meters, energy controllers, communication systems, decisive to facilitate the coordination of efficiency and DR in a smart grid, are described and discussed with reference to real industrial case studies and research projects. <s> BIB006 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <s> The growth of advanced metering infrastructure, enhanced communication infrastructure in power grids, and the ability of end-user consumer to actively participate helps in realizing vision of sustainable energy systems. Demand response (DR) programs are developed in order to deploy this ability and make power grids more efficient, environmental friendly, and reliable. This paper presents a review of DR, existing application and a possible implementation strategy in a smart grid environment. Furthermore, classification and status of DR programs in different U.S. electricity markets have been also discussed. <s> BIB007 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> BIB008 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> BIB009 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> BIB010 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> BIB011 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> BIB012 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> BIB013 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <s> In modern electric power grids, microgrids are utilized in order to improve electric power grids' technical, economical, and environmental performance. In other words, both operator and consumers benefit the gained improvements by microgrids. However, operators confront certain ordeals to plan and operate the specific microgrid. In this paper, Combined Heat and Power (CHP) units, renewable energy resources, auxiliary thermal units and Electric Vehicles (EVs) are taken into consideration as a specific microgrid in California in the United States. Also, stochastic price of gas and electricity is taken into account. Moreover, uncertainty of the output power associated with non-dispatchable units like wind power plants as well as stochastic availability of the EVs adds to the complexity of the problem. The proposed approach plans to minimize total cost associated with the utilities as well as to minimize released emissions into the environment. Hence, the mentioned problem is a Multi-objective Linear Programming (MIP) which is solved using GAMS software. Finally, it has been showed that obtained solution is robust against uncertain parameters since there is just insignificant difference between solutions under this condition compared to condition that stochastic parameters are not modeled. <s> BIB014 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> BIB015 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> BIB016 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> BIB017 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> BIB018 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> BIB019 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> BIB020 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <s> The optimization of operating times and operation modes of devices and systems that consume or generate electricity in buildings by building energy management systems promises to alleviate problems arising in today’s electricity grids. Conflicting objectives may have to be pursued in this context, giving rise to a multi-objective optimization problem. This paper presents the optimization of appliances as well as heating and air-conditioning devices in two distinct settings of smart buildings, a residential and a commercial building, with respect to the minimization of energy costs, CO2 emissions, discomfort, and technical wearout. We propose new encodings for appliances that are based on a combined categorization of devices respecting both, the optimization of operating times as well as operation modes, e.g., of hybrid devices. To identify an evolutionary algorithm that promises to lead to good optimization results of the devices in our real-world lab environments, we compare four state-of-the-art algorithms in realistic simulations: ESPEA, NSGA-II, NSGA-III, and SPEA2. The results show that ESPEA and NSGA-II significantly outperform the other two algorithms in our scenario. <s> BIB021 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <s> Abstract Of the total electricity-generating capacity in the United States, 20% is dedicated to meet peak loads. Strategies to mitigate volatility in energy consumption have the potential to reduce the need for this surplus capacity. Here we investigate the potential for residential consumers to lower community-level peak demand through home energy management systems. We focus on the combination of air-conditioning use with the operation of time-shiftable appliances in the southern U.S. A centralized model predictive control (MPC) scheme minimizes peak air-conditioning (A/C) energy use by altering the thermostat set-points in individual homes. We simultaneously schedule the operation of time-shiftable appliances to further reduce the community peak load. The scheduling problem is formulated as a mixed-integer linear program (MILP) aimed at minimizing peak load under constraints that reflect the start times and allowed delays of individual appliances (e.g. dishwashers, washing machines, dryers) in each house. Using sample data collected from residential homes and consumer survey data located in Austin, TX, USA we show that the proposed integrated control and scheduling approach can minimize the peak load for the neighborhood by leveraging the physical differences and individual preferences between houses. On average, our framework is able to reduce the daily peak load for the group of houses by 25.5% (18.2 kW) when compared with the load for individually controlled thermostat settings and appliance start times. <s> BIB022 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> BIB023 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> BIB024 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <s> Along with the development of Smart Energy System (SES), the advancing popularity of hybrid energy appliances, such as micro-combined heat and power (CHP) and electric heaters, requires an overall energy management strategy to optimize the energy using while guaranteeing energy supply for both the electricity and heat demand. This paper based on the concept of Multi-Commodity Smart Energy System (MC-SES) proposes a self-sufficient hybrid energy model to minimize the energy exchange with the external electricity grid. Next to the optimization solution for this hybrid energy management problem, a greedy algorithm has been proposed to alleviate the high computational complexity of optimization searching. The optimization, direct allocation and greedy-based methods will all be analyzed, simulated and compared in this paper. <s> BIB025 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> BIB026 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <s> Microgrids are subsystems of the distribution grid operating as a single controllable system either connected or isolated from the grid. In this paper, a novel cooperative model predictive control (MPC) framework is proposed for urban districts comprising multiple microgrids sharing certain distributed energy resources (DERs). The operation of the microgrids, along with the shared DER, are coordinated such that the available flexibility sources are optimised and a common goal is achieved, e.g., minimizing energy exchanged with the distribution grid and the overall energy costs. Each microgrid is equipped with an MPC-based energy management system, responsible for optimally controlling flexible loads, heating systems, and local generation devices based on end-user preferences, weather-dependent generation and demand forecasts, energy prices, and technical and operational constraints. The proposed coordination algorithm is distributed and guarantees constraints satisfaction, cooperation among microgrids and fairness in the use of the shared resources, while addressing the issue of scalability of energy management in an urban district. Furthermore, the proposed framework guarantees an agreed cost saving to each microgrid. The described method is implemented and evaluated in a virtual testing environment that integrates accurate simulators of the microgrids. Numerical experiments show the feasibility, the computational benefits, and the effectiveness of the proposed approach. <s> BIB027 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> BIB028 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> BIB029 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> BIB030 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> BIB031 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> BIB032 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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> BIB033 </s> Energy management for user’s thermal and power needs: A survey <s> DRP <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
A DRP is defined by the U.S. Department of Energy (DOE) (U.S. Department of Energy, 2006) as the ''changes in electric use by end-use customers in response to changes in the price of electricity over time, or to give incentive payments designed to induce lower electricity use at times of high market prices or when grid reliability is jeopardized''. The goals of DRPs are twofold: on the one hand, the consumers can reduce their energy bills by modifying their normal consumption patterns according to market price variability; on the other hand, the utility can reduce the risk of bottlenecks along lines, improving the system reliability, and postponing expensive investments in new generation plant and increasing of infrastructure capacity (U.S. Department of Energy, 2006; BIB006 . DRPs can be distinguished in dispatchable and non-dispatchable BIB007 . The former group -often referred to as incentivebased -offers financial reward/penalty schemes to end-users willing to let the system operator reduce, curtail, or interrupt their delivery during periods of local reliability-threatening peak demand or high prices. The second group is based on offering end-users time-varying rates (e.g., real-time pricing, time-of-use tariffs, critical-peak pricing) to motivate them to modify their demand over time while saving money (U.S. Department of Energy, 2006; BIB006 BIB001 . Given the economic scheme, such DRPs are referred to as price-based. Some recent studies BIB017 propose energy-carrier-based DRPs, which give the users the possibility to decide which energy carrier is used for part of the load, based on price signals BIB017 or CO 2 signals . DRPs are included into the optimization problem by several researchers (see Table 6 ), with the aim of increasing system flexibility, potential cost savings, reducing environmental impact, and flattening out load profile over time BIB008 . Several studies consider shiftable electric load, often limited to specific appliances (e.g., IT equipment BIB018 , air conditioning (AC) and domestic appliances BIB019 BIB026 BIB020  BIB021 BIB033 BIB009 BIB027 BIB022 BIB010 BIB028 BIB011 BIB023 BIB029 BIB002 BIB012 BIB024 BIB025 BIB030 BIB003 BIB013 ; De Angelis et al., 2013), electric heat pump BIB034 , and electric vehicle BIB031 ) or to a fixed amount of the total demand in any time interval BIB004 . Also electric vehicles can offer a service for power peak shaving, as the charging process can be controlled and shifted in time, if needed  . Many studies assume curtailing the electric and thermal load as a viable option for balancing the system, even if no reward is usually offered to end-user for this service. By contrast, in  and BIB014 , the utility pays a fare of 0.04$/kWh and 1.2 times of the electricity price, respectively, to the users for participation in DRPs. The demand involved in curtailing is often the lighting system BIB015 BIB021 , as it can be dimmed, as well as the space heating and hot water demand, if a maximum temperature deviation is considered possible BIB015 BIB016 BIB034 BIB021 BIB033 BIB009 BIB027 BIB028 BIB032 BIB031 BIB029 BIB002 Jadid, 2015, 2017; BIB013 BIB005 . Variation in heating demand is interpreted as a procedure of preheating BIB018 or pre-cooling , where a maximum deviation of a couple of degrees from the desired temperature is allowed.