Energy management for user’s thermal and power needs: A survey <s> Grid model <s> Gas-based combined heat and power (CHP) has matured enough to be regarded as the next evolutionary step in promoting energy efficiency use in the urban environment. Although its potential market is increasing, little research has been conducted into the combined technical effects that a high penetration of these units may have on both natural gas and electric (G&E) distribution networks. This paper presents a power flow tool that performs a simultaneous assessment on some technical impacts that a high penetration of heat-driven cogeneration units may have on G&E networks. A case study is presented and results show that as expected, the gas demand increases as well as the losses associated with its delivery, while the opposite effects occur in the electrical system. However, less evident is the load profile variations distribution networks will experience and that overall energy losses will vary according to the CHP penetration and the type of technology used. The study shows that an integrated G&E analysis offers a fresh perspective in quantifying the effects cogeneration technologies will have on energy distribution networks. <s> BIB001 </s> Energy management for user’s thermal and power needs: A survey <s> Grid model <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> BIB002 </s> Energy management for user’s thermal and power needs: A survey <s> Grid model <s> This paper considers heat and power production from micro Combined Heat and Power systems and heat storage in a network of households. The goal is to balance the local heat demand and supply in combination with balancing the power supply and demand in the network. The on-off decisions of the local generators are done completely distributed based on local information and information exchange with a few neighbors in the network. This is achieved by using an information sharing model with a distributed model predictive control method based on dual-decomposition and sub-gradient iterations. Because of the binary nature of the decisions, a mixed integer quadratic problem is solved at each agent. The approach is tested with simulation, using realistic heat and power demand patterns. We conclude that the distributed control approach is suitable for embedding the distributed generation at a household level. <s> BIB003 </s> Energy management for user’s thermal and power needs: A survey <s> Grid model <s> A smart house generally has a Photovoltaic panel (PV), a Heat Pump (HP), a Solar Collector (SC) and a fixed battery. Since the fixed battery can buy and store inexpensive electricity during the night, the electricity bill can be reduced. However, a large capacity fixed battery is very expensive. Therefore, there is a need to determine the economic capacity of fixed battery. Furthermore, surplus electric power can be sold using a buyback program. By this program, PV can be effectively utilized and contribute to the reduction of the electricity bill. With this in mind, this research proposes a multi-objective optimization, the purpose of which is electric demand control and reduction of the electricity bill in the smart house. In this optimal problem, the Pareto optimal solutions are searched depending on the fixed battery capacity. Additionally, it is shown that consumers can choose what suits them by comparing the Pareto optimal solutions. <s> BIB004 </s> Energy management for user’s thermal and power needs: A survey <s> Grid model <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> BIB005 </s> Energy management for user’s thermal and power needs: A survey <s> Grid model <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> BIB006 </s> Energy management for user’s thermal and power needs: A survey <s> Grid model <s> Abstract Based on the kind of fuel consumed by generation unit, each generation system has different generation costs and emits various types of greenhouse gases like CO 2 , SO 2 and NO 2 to the atmosphere. So, nowadays in the power system scheduling, emission issue has been turned to be an important factor. In this paper, in addition to economic performance, emission problem of energy hub system has been also investigated. Therefore, a multi-objective optimization model has been proposed for cost-environmental operation of energy hub system in the presence of demand response program (DRP). Weighted sum approach has been employed to solve the proposed multi-objective model and fuzzy satisfying technique has been implemented to select the best compromise solution. Implementation of load management programs presented by DRP shifts some percentage of load from peak periods to off-peak periods to flatten load curve which leads to reduction of total cost and emission of energy hub system. A mixed-integer linear programming has been used to model the cost-environmental performance problem of energy hub system and then, GAMS optimization software has been utilized to solve it. A sample energy hub system has been studied and the obtained results have been compared to validate the effectiveness of proposed techniques. <s> BIB007 </s> Energy management for user’s thermal and power needs: A survey <s> Grid model <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> BIB008 </s> Energy management for user’s thermal and power needs: A survey <s> Grid model <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> BIB009 </s> Energy management for user’s thermal and power needs: A survey <s> Grid model <s> Abstract The ongoing decentralization of the electrical power system encompasses the installation of residential electrical loads and generators, such as electro-thermal heating devices. Clusters of these devices in combination with thermal storage can play a significant role in future power system control as they introduce load and generation flexibility into the system. Hence, control concepts which are attractive for both power system level and end user side are required. This work proposes a novel decentralized scheduling approach for electro-thermal heating devices based on a multi-agent system architecture. As a major novelty, the approach addresses both of the aforementioned appeals on two levels without central control. First, local planning with individual objectives is performed. Second, the heating devices cooperate to find the combination of local plans serving best a given energy system objective. This approach is fully decentralized and has a strong end user involvement. The performance of the proposed approach related to end user and system level objectives is compared to that of centralized approaches. Simulation results show that the new method can successfully satisfy end user objectives and provide energy fluctuation balancing. Finally, drawbacks compared to centralized scheduling are analyzed. <s> BIB010 </s> Energy management for user’s thermal and power needs: A survey <s> Grid model <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> BIB011 </s> Energy management for user’s thermal and power needs: A survey <s> Grid model <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> BIB012 </s> Energy management for user’s thermal and power needs: A survey <s> Grid model <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> BIB013 </s> Energy management for user’s thermal and power needs: A survey <s> Grid model <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> BIB014 </s> Energy management for user’s thermal and power needs: A survey <s> Grid model <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> BIB015 </s> Energy management for user’s thermal and power needs: A survey <s> Grid model <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> BIB016
Power lines as well as pipes have physical characteristics determining the maximum amount of energy they can carry and energy losses during transportation and distribution. In power systems the amount of power a line or a cable can carry is limited, due to constraints over voltage drop and thermal effects on conductors and system equipment. Moreover, transmitting electricity between two nodes of the grid causes power losses along the lines mainly due to conductors' resistance  . Similarly, the type of pipes and the maximum allowed pressure drop determine the maximum gas flow, and friction causes energy losses BIB001 . Grid infrastructure can significantly affect the operation, as electricity (and gas) flow is determined by the line (pipe) parameters and voltage (pressure) difference between nodes BIB001 . Given the complexity of these infrastructures, we can distinguish different approaches, as summarized in Table 7 . The most common approach is to neglect the grid structure and consider one for which the MES and stand-alone DERs, if any, are connected in one single point, where all energy exchanges with the main grid occur. This model is referred to as ''single-busbar model'' (SBB) Connections between these entities and the main grid can be modeled as constrained, i.e., a maximum amount of power can be exchanged during a time interval, or unconstrained, if this limitation is neglected. When a constrained approach is taken, there is a limit on the maximum power that can be exchanged with the main grid. Further constraints can be imposed on the exchanged power variation between two consecutive time steps BIB004 or according to the transformer rated capacity BIB007 . Constraints are defined for both active and reactive power in BIB016 in order to fulfill the network capacities. The hourly amount of gas imported from the main network can also be limited within a certain range BIB007 BIB008 BIB014 BIB009 BIB005 , as well as the imported water BIB007 . Multi-energy systems may be connected not only to the main grid, but also among each other to exchange locally produced energy BIB006 BIB014 . These connections can be either unconstrained BIB006 BIB002 BIB010 BIB003 BIB011 or constrained. The studies adopting this approach belong to the ''Interconnected'' groups and are listed in Table 7 . Four residential units are connected in BIB006 in order to exchange both electricity and heat, sharing FC-CHP systems, back-up boilers, and storage devices. Larger systems are simulated in BIB002 , BIB003 and BIB011 , where ten to thirty smart homes that can exchange power, improving the integration of CHP units and/or PV panels, whereas almost 150 homes are considered in BIB010 . The counter approach is to locate sources and loads within the grid and consider how the energy is dispatched along the lines following the available connections. In this case, the physical infrastructure of the grid is taken into account, although is it usually simplified. The transmission grid is modeled as a graph in BIB012 , where the nodes represent power plants, both traditional and renewable, and aggregated loads. The power flows are constrained by the capacity of the lines and their physical connections, which are derived from two IEEE-buses. Active power losses are calculated in BIB015 by running a load flow on a radial grid; then, such losses are allocated to each node, based on its position within the grid. A constrained power flow analysis is run on a modified version of the IEEE-13-node distribution feeder in BIB013 , in order to evaluate the feasibility of building load profile. The Only studies that explicitly refer to a fuel supply grid are included in the Table. technical limits of lines, transformers, and capacitors are taken into account. As for the fuel grid, the MESs do not usually produce fuel, hence the only possible connection is with the main distribution grid and it can be constrained or unconstrained. In Table 7 , only studies that explicitly refer to a fuel supply grid are included. Some studies include the possibility to exchange thermal power in a heat network, such as BIB006 , BIB014 ,  and BIB007 