A Survey on Home Energy Management <s> B. CENTRALISED VS DISTRIBUTED MANAGEMENT <s> Demand side management encourages the users in a smart grid to shift their electricity consumption in response to varying electricity prices. In this paper, we propose a distributed framework for the demand response based on cost minimization. Each user in the system will find an optimal start time and operating mode for the appliances in response to the varying electricity prices. We model the cost function for each user and the constraints for the appliances. We then propose an approximate greedy iterative algorithm that can be employed by each user to schedule appliances. In the proposed algorithm, each user requires only the knowledge of the price of the electricity, which depends on the aggregated load of other users, instead of the load profiles of individual users. In order for the users to coordinate with each other, we introduce a penalty term in the cost function, which penalizes large changes in the scheduling between successive iterations. Numerical simulations show that our optimization method will result in lower cost for the consumers, lower generation costs for the utility companies, lower peak load, and lower load fluctuations. <s> BIB001 </s> A Survey on Home Energy Management <s> B. CENTRALISED VS DISTRIBUTED MANAGEMENT <s> In this paper, a novel distributed iterative adaptive dynamic programming (ADP) method is developed to solve the multibattery optimal coordination control problems for home energy management systems. According to system transformations, the multi-input optimal control problem is transformed into a single-input optimal control problem, where all the batteries are implemented at their worst performance. Next, based on the worst-performance optimal control law, an effective distributed iterative ADP algorithm is developed, where, in each iteration, only a single-input optimization problem is implemented. Convergence properties of the distributed iterative ADP algorithm are developed to show that the iterative performance index function converges to the optimum. Finally, numerical analysis is given to illustrate the performance of the developed algorithm. <s> BIB002 </s> A Survey on Home Energy Management <s> B. CENTRALISED VS DISTRIBUTED MANAGEMENT <s> This study proposes a novel coordination mechanism for smart homes in a small-scale neighborhood. This mechanism deploys a price and incentive-based energy management algorithm that considers renewable energy sources and battery storage. The objective of the proposed coordination algorithm is to decrease the electricity bills of the smart homes by increasing renewable energy usage inside the neighborhood, and enabling electricity trading among smart homes. The neighborhood is modeled using multi-agent systems, and heuristic problem solving is used by deploying a genetic optimization and rolling-horizon technique for day-ahead scheduling of the electricity appliances. Simulation results show that neighborhood coordination combined with incentives enable decreasing the total cost of neighborhood electricity consumption by increasing local renewable energy usage. Also, consumer results show that each individual smart home benefits from using the coordination mechanism. <s> BIB003 </s> A Survey on Home Energy Management <s> B. CENTRALISED VS DISTRIBUTED MANAGEMENT <s> In this paper, the problem of distributed home energy management system with storage ( HoMeS ) in a coalition, which consists of multiple microgrids and multiple customers, is studied using the multiple-leader–multiple-follower Stackelberg game theoretic model—a multistage and multilevel game. The microgrids, which act as the leaders, need to decide on the minimum amount of energy to be generated with the help of a central energy management unit and the optimum price per unit energy to maximize their profit. On the other hand, the customers, which act as the followers, need to decide on the optimum amount of energy to be consumed, including the energy to be requested for storage. Using the proposed distributed scheme, i.e., HoMeS , the earned profit of the grid improves up to 55%, and the customers consume almost 30.79% higher amount of energy, which, in turn, increases the utilization of the generated energy by the microgrids. <s> BIB004 </s> A Survey on Home Energy Management <s> B. CENTRALISED VS DISTRIBUTED MANAGEMENT <s> This paper introduces a day-ahead energy management algorithm for the coordination of smart homes with renewable energy sources and energy storage systems in neighborhood areas. The aim of this paper is to establish a day-ahead decentralized coordination method with appliance scheduling and energy sharing (among smart homes) to minimize the electricity bills of consumers under dynamic pricing. The energy sharing algorithm focuses on increasing the utilization of renewable sources by controlling storage units. A multi-agent system is used to model entities (smart homes, aggregator, and utility) as agents and the optimization problem is solved in a decentralized manner by home agents with a genetic algorithm. The performance of the coordination algorithm is evaluated annually with and without considering forecasting errors. <s> BIB005 </s> A Survey on Home Energy Management <s> B. CENTRALISED VS DISTRIBUTED MANAGEMENT <s> Abstract Demand response is a promising technology to cope with the ever-increasing peak energy consumption in smart grid. For the smart home energy management system, it may choose to charge the battery when the electricity price is low, and discharge the battery to supply the home load when the electricity price is high. Under this circumstance, the temporally and spatially coupled constraints coexist in the energy demand, battery charging and discharging energy, and energy supply. The coupled constraints make the demand response problem more challenging than the existing ones and thus the existing distributed algorithms cannot be employed to find the optimal solution of the problem. In this paper, we focus on the real-time two-way communications between utility company and multiple residential users, and each user is regarded as a smart home energy management system. A distributed real-time algorithm is proposed to find the optimal energy management scheduling scheme for each user and utility company to maximize the social welfare. To overcome the obstacle brought by the temporally and spatially coupled constraints, dual decomposition technique is employed and the primal problem is decoupled into several independent subproblems which can be solved in a distributed way by each user and utility company without revealing or exchanging their private information. Simulation results are provided to demonstrate that the proposed distributed algorithm can bring potential benefits to the society. <s> BIB006
Although HEMSs are often deployed to reduce electricity bill at demand-side, they can never operate in complete isolation from the grid and customers. The reason is related to the fact that the grid needs to ensure adequate supply to multiple customers under a dynamic load demand, namely by deploying additional generators. Furthermore, a HEMS needs to be aware of the demand required by other buildings, so that high demand stress on the grid is avoided, in particular during unexpected periods of time. If, for example, households start shifting many loads to night periods, the grid infrastructure may not be able to match demands within feasible time. This could result in power outages and blackouts, and therefore coordinated energy management is imperative. Two approaches can be considered, namely centralised and distributed BIB005 . Centralised HEMSs implement methodologies for coordination of consumption and generation on a platform, which is located at the utility level. In this context, the central platform, mentioned in Section II-A, no longer handles appliance scheduling. It strictly assumes sensor data aggregation, processing and inference tasks. Under this topology, buildings should send to the grid consumption related information, including sensor data, appliance operation needs and constraints, just to name out a few. Next, taking this information into account, the grid centralised management platform schedules demand-side electricity and generator operations in order to optimise specific criteria, for instance operational costs, or peak-to-average ratio. This leads to a massive constrained optimisation problem, with the corresponding heavy computational burden. Unlike centralised methodologies, distributed-based approaches rely on several independent decision-making entities to plan demand-side and grid operations. Commonly, they cooperate with one another and the grid to find a mutual agreement feasible solution that maximises individual goals, without compromising other decision-makers' goals or power supply stability. As grid resources are managed in a distributed way, the underlying computational burden is shared among all players, resulting in a significantly lower individual computational overhead. This makes distributed strategies very appealing, particularly in smart grid scenarios where many different assets need to be coordinated. In order to reach a global consensus, it demands frequent communication among participants. Despite the inherent increase in the volume of transmitted data over communication networks, sensitive information is typically not exchanged as much as for centralised-based scheduling. For example, instead of transmitting individual appliance operation needs and constraints, decision-makers should only provide the minimum required power level BIB004 . Decision-makers are mainly modelled by intelligent agents in game-theory and multiagent-based techniques BIB005 - BIB003 , or even mathematical optimisation-based approaches BIB001 - BIB006 . It should be mentioned that, although distributed energy management-based techniques are applied to groups of buildings BIB005 , BIB004 , BIB001 , they can also be used for energy management at individual buildings level BIB002 . In such cases, agents are assigned a single appliance and are responsible for managing its operation.