This paper, by comparing three potential energy trading systems, studies the feasibility of integrating a community energy storage (CES) device with consumer-owned photovoltaic (PV) systems for demand-side management of a residential neighborhood area network. We consider a fully-competitive CES operator in a non-cooperative Stackelberg game, a benevolent CES operator that has socially favorable regulations with competitive users, and a centralized cooperative CES operator that minimizes the total community energy cost. The former two game-theoretic systems consider that the CES operator first maximizes their revenue by setting a price signal and trading energy with the grid. Then the users with PV panels play a non-cooperative repeated game following the actions of the CES operator to trade energy with the CES device and the grid to minimize energy costs. The centralized CES operator cooperates with the users to minimize the total community energy cost without appropriate incentives. The non-cooperative Stackelberg game with the fully-competitive CES operator has a unique Stackelberg equilibrium at which the CES operator maximizes revenue and users obtain unique Pareto-optimal Nash equilibrium CES energy trading strategies. Extensive simulations show that the fully-competitive CES model gives the best trade-off of operating environment between the CES operator and the users.
The Stackelberg game between a shared-facility controller and users in @cite_26 yields effective demand-side management by managing consumer demand with an energy storage device at the controller-side that is enabled to charge and discharge with the grid. In contrast, in this paper, the charging and discharging mechanism of the CES device is intended to accommodate energy trading strategies from PV energy generation of users. In doing so, we focus on exploiting onsite energy generation from user-owned PV systems for demand-side management as an alternative to energy consumption scheduling of users.
@cite_26
2339586602 2023865237 2016038157 2208121241
This paper develops a novel energy trading system with a community energy storage (CES) device for demand-side load management within a neighborhood area network. The energy users in the proposed system that have their own photovoltaic power generation are allowed to trade energy from their personal surplus with the grid and the CES device. We adopt a dynamic noncooperative repeated game with Pareto-efficient pure strategies as the decentralized approach for the users to determine optimal energy trading amounts for the next day. This decentralized model needs minimal information exchange or communication between users. Simulation results show that our system is able to provide peak load leveling for the grid, while providing financial benefits to users. Moreover, the performance benefits of our system are robust to both inaccuracy in day-ahead power forecasts and CES battery inefficiencies. We study the demand side management (DSM) problem when customers are equipped with energy storage devices. Two games are discussed: the first is a non-cooperative one played between the residential energy consumers, while the second is a Stackelberg game played between the utility provider and the energy consumers. We introduce a new cost function applicable to the case of users selling back stored energy. The non-cooperative energy consumption game is played between users who schedule their energy use to minimize energy cost. The game is shown to have a unique Nash equilibrium, that is also the global system optimal point. In the Stackelberg game, the utility provider sets the prices to maximize its profit knowing that users will respond by minimizing their cost. We provide existence and uniqueness results for the Stackelberg equilibrium. The Stackelberg game is shown to be the general case of the minimum Peak-to-Average power ratio (PAR) problem. Two algorithms, centralized and distributed, are presented to solve the Stackelberg game. We present results that elucidate the interplay between storage capacity, energy requirements, number of users and system performance measured in total cost and peak-to-average power ratio (PAR). Efficient and reliable demand side management techniques for community charging of plug-in hybrid electrical vehicles (PHEVs) and plug-in electrical vehicles (PEVs) are needed, as large numbers of these vehicles are being introduced to the power grid. To avoid overloads and maximize customer preferences in terms of time and cost of charging, a constrained nonlinear optimization problem can be formulated. In this paper, we have developed a novel cooperative distributed algorithm for charging control of PHEVs PEVs that solves the constrained nonlinear optimization problem using Karush-Kuhn-Tucker (KKT) conditions and consensus networks in a distributed fashion. In our design, the global optimal power allocation under all local and global constraints is reached through peer-to-peer coordination of charging stations. Therefore, the need for a central control unit is eliminated. In this way, single-node congestion is avoided when the size of the problem is increased and the system gains robustness against single-link node failures. Furthermore, via Monte Carlo simulations, we have demonstrated that the proposed distributed method is scalable with the number of charging points and returns solutions, which are comparable to centralized optimization algorithms with a maximum of 2 sub-optimality. Thus, the main advantages of our approach are eliminating the need for a central energy management coordination unit, gaining robustness against single-link node failures, and being scalable in terms of single-node computations. In this paper, we propose a model for households to share energy from community energy storage (CES) such that both households and utility company benefit from CES. In addition to providing a range of ancillary grid services, CES can also be used for demand side management, to shave peaks and fill valleys in system load. We introduce a method stemming from consumer theory and cooperative game theory that uses CES to balance the load of an entire locality and manage household energy allocations respectively. Load balancing is derived as a geometric programming problem. Each households contribution to overall non-uniformity of the load profile is modeled using a characteristic function and Shapley values are used to allocate the amount and price of surplus energy stored in CES. The proposed method is able to perfectly balance the load while also making sure that each household is guaranteed a reduction in energy costs.