Energy management for user’s thermal and power needs: A survey <s> Multi-agent <s> Aligned towards net zero energy building goals, building energy management systems will consider energy utilization efficiency improvement, energy cost reduction and renewable energy technology utilization to serve the local energy loads in building structures with dispersed resources. The distributed management of building energy system in this paper describes a semi-centralized decision making methodology using multi-agent systems for building energy management system in electrical, heating and cooling energy zones with combined heat and power system optimization aimed at improving energy efficiency and reducing energy cost. The semi-centralized decision making process will be implemented in a case study to pursue minimum energy cost. <s> BIB001 </s> Energy management for user’s thermal and power needs: A survey <s> Multi-agent <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> BIB002 </s> Energy management for user’s thermal and power needs: A survey <s> Multi-agent <s> Multiple energy carrier systems stem from the need to evolve traditional electricity, gas and other energy systems to more efficient, integrated energy systems. An approach is presented, for controlling multiple energy carriers, including electricity (AC or DC), heat, natural gas and hydrogen, with the objective to minimise the overall cost and/or emissions, while adhering to technical and commercial constraints, such as network limits and market contracts. The technique of multi-agent systems (MAS) was used. The benefits of this approach are discussed and include a reduction of more than 50% in the balancing costs of a potential deviation. An implementation of this methodology is also presented. In order to validate the operation of the developed system, a number of experiments were performed using both software and hardware. The results validated the efficient operation of the developed system, proving its ability to optimise the operation of multiple energy carrier inputs within the context of an energy hub, using a hierarchical multi-agent system control structure. <s> BIB003 </s> Energy management for user’s thermal and power needs: A survey <s> Multi-agent <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> BIB004 </s> Energy management for user’s thermal and power needs: A survey <s> Multi-agent <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> BIB005 </s> Energy management for user’s thermal and power needs: A survey <s> Multi-agent <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> BIB006
Beside single-and multi-objective problems, some studies propose a multi-agent scheduling problem. In this approach, each agent has its own goal, while resources to be scheduled are shared. The agents may act on the same environment, e.g., the same space where three main energy zones are identified, namely electricity, cooling, and heating zone, as in BIB001 , or they can act in different energy systems, while being part of the same cluster, as in BIB004 , BIB005 , BIB002 , BIB006 and BIB003 . The idea is to achieve a global objective by coordination and exchange of information among the agents. In BIB001 , the ultimate goal is to minimize the energy costs, which is achieved by optimizing the technical objectives of three agents. In particular, the heating agent aims at maximizing the efficiency of the heating system, so that less natural gas has to be burned to produce hot water. Similarly, the cooling agent has to maximize the efficiency of the cooling devices; the electric agent has to reduce the peak electric load and communicate and coordinate the system with the main grid. In BIB004 and BIB005 , the optimization problem is divided into two levels, a local one and a cluster one. In BIB004 , each building energy system minimizes the number of switching events of their heating devices, so that efficiency is improved and the stress of each device is reduced. Then, the cluster level minimized the fluctuation of the energy demand, by modifying the number of switching events in each building within a certain range. In Anvari-Moghaddam et al. (2017) , each residential end-user corresponds to a building management agent, which aims at scheduling its appliances by finding a trade-off between minimizing the energy costs and the electrical and thermal discomfort levels. At a higher level, a centralized agent coordinates all MG agents, which include also RES agents, monitoring wind turbine (WT) and PV, and a battery bank agent. The goal of the EMS proposed by Larsen et al. is to operate CHP systems to minimize the overall power imbalance of a network which consists in a group of interconnected households BIB002 . The global objective is achieved while each agent aims at minimizing the local power imbalance, defined as the weighted sum of the changes in energy productions and in power demand between two consecutive time steps, and a share of the imbalance information of the neighbor agents. In BIB006 , a building controller aims at minimizing the electricity costs, while satisfying the non-dispatchable loads and managing the flexible ones. The resulting load profile is then sent to a distribution grid controller, which runs a power flow analysis to check the feasibility of the load profile in terms of maximum allowable load. If there is any infeasibility, the grid controller sends a feedback with the maximum allowable load to the building controller, and the load profile has to be adjusted accordingly.