Energy management for user’s thermal and power needs: A survey <s> A.6. Microgrids <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> A.6. Microgrids <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> BIB002 </s> Energy management for user’s thermal and power needs: A survey <s> A.6. Microgrids <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> BIB003 </s> Energy management for user’s thermal and power needs: A survey <s> A.6. Microgrids <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> BIB004 </s> Energy management for user’s thermal and power needs: A survey <s> A.6. Microgrids <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> BIB005 </s> Energy management for user’s thermal and power needs: A survey <s> A.6. Microgrids <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> BIB006 </s> Energy management for user’s thermal and power needs: A survey <s> A.6. Microgrids <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> BIB007 </s> Energy management for user’s thermal and power needs: A survey <s> A.6. Microgrids <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> BIB008 </s> Energy management for user’s thermal and power needs: A survey <s> A.6. Microgrids <s> Abstract A model predictive control (MPC) based coordinated operation framework for a grid-connected residential microgrid with considering forecast errors is presented in this paper. This residential microgrid composes renewable energy resources (e.g., wind and solar), distributed generators (e.g., CHP), energy storages (e.g., battery bank and water tank), electrical vehicle, and smart loads (e.g. HVAC and washing machine). A novel mixed integer linear programming (MILP) problem is optimized at each decision time, on the basis of the short-term forecasts of renewable energy resources generation, load demand, and electricity price. This MILP problem is integrated into a MPC framework to reduce the negative impacts of forecast errors. Case study which considers forecast uncertainties is implemented for evaluating the performance of the proposed method and the traditional method is used. Besides, peak power price mechanism which is used to smooth the power exchanged with external grid is also considered. Moreover, a further sensitivity analysis is realized in order to discuss the impacts of energy storage units on the microgrid operation. Simulation results show that the proposed method is economic and flexible. <s> BIB009 </s> Energy management for user’s thermal and power needs: A survey <s> A.6. Microgrids <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> BIB010 </s> Energy management for user’s thermal and power needs: A survey <s> A.6. Microgrids <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> BIB011 </s> Energy management for user’s thermal and power needs: A survey <s> A.6. Microgrids <s> This paper presents a probabilistic Unit Commitment (UC) model for optimal scheduling of wind power, load forecasts and controllability of vehicles in a microgrid using a stochastic programming framework. The microgrid is made up of microturbines, wind turbine, boiler, Plug-in Electric Vehicles (PEVs), thermal storage and battery storage. The proposed model will help the power grid operators with optimal day- ahead planning even with variable operating conditions in respect of load forecasts, controllability of vehicles and wind generation. A set of valid scenarios is assigned for the uncertainties of wind sources, load and PEVs and objective function in the form of expected value. The objective function is to maximize the expected total profit of the UC schedule for the set of scenarios from the viewpoint of microgrid management. The probabilistic unit commitment optimizes the objective function using Particle Swarm Optimization (PSO) algorithm. In order to verify the effectiveness of the stochastic modelling and make a comparison with a simple deterministic one, a typical microgrid is used as a case study. The results can be used to evaluate the effect of integration of PEVs on the economic operation of the microgrid. The results also confirm the necessity to consider the key uncertainties of the microgrid; otherwise the results could overly misrepresent the real world operation of the system. <s> BIB012 </s> Energy management for user’s thermal and power needs: A survey <s> A.6. Microgrids <s> This paper proposes real-time Energy Management System (EMS) for a residential hybrid ac/dc microgrid. The residential microgrid is organized in two different distribution systems. A dc distribution bus which interconnect the renewable energy sources (RES), energy storage systems (ESS) and the building's common facilities; while the apartments are supplied by an ac distribution system connected to the grid. This architecture avoids any modifications in the electrical installation that supplies energy to the apartments. A pure dc voltage supply is not yet a feasible approach for residential buildings. This architecture increases the overall efficiency of the distribution by interconnecting the RES and ESS thorough a dc distribution bus, and therefore avoiding unnecessary dc/ac conversion stages. The real-time EMS performs an 24 hours ahead optimization in order to schedule the charge/discharge of the ESS, and the energy injection/consumption from the grid. The EMS estimates the RES generation based on the weather forecasting, together with stochastic consumption modelling of the building. The EMS architecture and the residential microgrid have been implemented and tested in a laboratory scale setup. The results shown how the operational costs of the system are effectively decreased by 28%, even with non-accurate estimation of the RES generation or building parameters. <s> BIB013 </s> Energy management for user’s thermal and power needs: A survey <s> A.6. Microgrids <s> Renewable energy resources are often known as cost-effective and lucrative resources and have been widely developed due to environmental-economic issues. Renewable energy utilization even in small scale (e.g., microgrid networks) has attracted significant attention. Energy management in microgrid can be carried out based on the generating side management or demand side management. In this paper, portable renewable energy resource are modeled and included in microgrid energy management as a demand response option. Utilizing such resources could supply the load when microgrid cannot serve the demand. This paper addresses energy management and scheduling in microgrid including thermal and electrical loads, renewable energy sources (solar and wind), CHP, conventional energy sources (boiler and micro turbine), energy storage systems (thermal and electrical ones), and portable renewable energy resource (PRER). Operational cost of microgrid and air pollution are considered as objective functions. Uncertainties related to the parameters are incorporated to make a stochastic programming. The proposed problem is expressed as a constrained, multi-objective, linear, and mixed-integer programing. Augmented Epsilon-constraint method is used to solve the problem. Final results and calculations are achieved using GAMS24.1.3/CPLEX12.5.1. Simulation results demonstrate the viability and effectiveness of the proposed method in microgrid energy management. <s> BIB014 </s> Energy management for user’s thermal and power needs: A survey <s> A.6. Microgrids <s> In this paper, an ontology-driven multi-agent based energy management system (EMS) is proposed for monitoring and optimal control of an integrated homes/buildings and microgrid system with various renewable energy resources (RESs) and controllable loads. Different agents ranging from simple-reflex to complex learning agents are designed and implemented to cooperate with each other to reach an optimal operating strategy for the mentioned integrated energy system (IES) while meeting the system’s objectives and related constraints. The optimization process for the EMS is defined as a coordinated distributed generation (DG) and demand response (DR) management problem within the studied environment and is solved by the proposed agent-based approach utilizing cooperation and communication among decision agents. To verify the effectiveness and applicability of the proposed multi-agent based EMS, several case studies are carried out and corresponding results are presented. <s> BIB015 </s> Energy management for user’s thermal and power needs: A survey <s> A.6. Microgrids <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> BIB016 </s> Energy management for user’s thermal and power needs: A survey <s> A.6. Microgrids <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> BIB017
A MG is defined by the DOE as ''a group of interconnected loads and distributed energy resources within clearly defined electrical boundaries that acts as a single controllable entity with respect to the grid. A MG can connect and disconnect from the grid to enable it to operate in both grid-connected or islanded-mode''  . The concept of MG can be extended to include not only electricity, but also thermal power. For instance, the work in BIB004 presents a simulation of both online and off-line operation of a MG and evaluate its flexibility in reducing operational costs and emission, while supplying flexible electrical and thermal demands. Similarly, several works manage CHP-based MGs by finding the optimal set point of DERs and by applying DR programs to reshape the load profile (e.g., BIB005 BIB006 BIB001 BIB007 BIB002 BIB008 BIB013 BIB009 . It is important to underline that some of the reviewed studies on MGs actually do not investigate the possibility of islanding operation, while keeping the characteristic of being a cluster of DERs seen by the main grid as a single entity BIB014 BIB001 BIB015 BIB010  BIB003 BIB012 BIB011 BIB016 BIB013 BIB017 BIB009 .