Flexibility in power demand, diverse usage patterns and storage capability of plug-in electric vehicles (PEVs) grow the elasticity of residential electricity demand remarkably. This elasticity can be utilized to form the daily aggregated demand profile and or alter instantaneous demand of a system wherein a large number of residential PEVs share one electricity retailer or an aggregator. In this paper, we propose a demand response (DR) technique to manage vehicle-to-grid (V2G) enabled PEVs' electricity assignments (charging and discharging) in order to reduce the overall electricity procurement costs for a retailer bidding to a two-settlement electricity market, i.e., a day-ahead (DA) and a spot or real-time (RT) market. We show that our approach is decentralized, scalable, fast converging and does not violate users' privacy. Extensive simulations show significant overall cost savings can be achieved for a retailer bidding to an operational electricity market by using the proposed algorithm. This technique becomes more needful when the power grid accommodates a large number of intermittent energy resources wherein RT demand altering is crucial due to more likely contingencies and hence more RT price fluctuations and even occurring the so-called . Finally, such retailer could offer better deals to customers as well.
In @cite_4 , charging and discharging of PEVs are managed in order to maximize the social and individual welfare functions. However, in residential sector, defining appropriate utility and welfare functions for the individual users is very ambiguous.
@cite_4
2016038157 1964861297 2004264786 1504773607
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. The paper develops and illustrates a novel decentralized charging control algorithm for large populations of plug-in electric vehicles (PEVs). The proposed algorithm is an application of the so-called Nash certainty equivalence principle (or mean-field games.) The control scheme seeks to achieve social optimality by establishing a PEV charging schedule that fills the overnight demand valley. The paper discusses implementation issues and computational complexity, and illustrates concepts with various numerical examples. Constrained charging control of large populations of Plug-in Electric Vehicles (PEVs) is addressed using mean field game theory. We consider PEVs as heterogeneous agents, with different charging constraints (plug-in times and deadlines). The agents minimize their own charging cost, but are weakly coupled by the common electricity price. We propose an iterative algorithm that, in the case of an infinite population, converges to the Nash equilibrium associated with a related decentralized optimization problem. In this way we approximate the centralized optimal solution, which in the unconstrained case fills the overnight power demand valley, via a decentralized procedure. The benefits of the proposed formulation in terms of convergence behavior and overall charging cost are illustrated through numerical simulations. In this paper, the problem of optimally charging or discharging customer-owned storage units in a smart grid is studied. In particular, a noncooperative game is formulated between customers equipped with distributed storage units who seek to decide on their optimal charging strategy so as to optimize a utility that reflects both the monetary benefit from the charging discharging operation and the associated penalty from power regulation. In contrast to the traditional game-theoretic models that are often based on rational and objective decisions, we introduce the behavioral framework of prospect theory (PT) so as to explicitly incorporate how the customers estimate their utilities with respect to their real-world considerations of gains and losses. The existence of the mixed-strategy Nash equilibrium is shown for the PT case and the properties of the game are analyzed under both PT and classical game theory. Simulation results show how the individual customer perceptions on their utilities can significantly impact the overall charging and discharging operations.