Optimal Real-Time Coordination of Energy Storage Units As a Voltage-Constrained Game

Abstract

With increasingly favorable economics and bundling of different grid services, energy storage systems (ESS) are expected to play a key role in integrating renewable generation. This work considers the coordination of ESS owned by customers located at different buses of a distribution grid. Customers participate in frequency regulation and experience energy prices that increase with the total demand. Charging decisions are coupled across time due to battery dynamics, as well as across network nodes due to competitive pricing and voltage regulation constraints. Maximizing the per-user economic benefit while maintaining voltage magnitudes within allowable limits is posed here as a network-constrained game. It is analytically shown that a generalized Nash equilibrium exists and can be expressed as the minimizer of a convex yet infinite-time horizon aggregate optimization problem. To obtain a practical solution, a Lyapunov optimization approach is adopted to design a real-time scheme offering feasible charging decisions with performance guarantees. The proposed method improves over the standard Lyapunov technique via a novel weighting of user costs. By judiciously exploiting the physical grid response, a distributed implementation of the real-time solver is also designed. The features of the novel algorithmic designs are validated using numerical tests on realistic datasets.

Network-Aware Demand-Side Management Framework With A Community Energy Storage System Considering Voltage Constraints

“…where φ t , δ t > 0 can be obtained through a day-ahead electricity market clearing process [18]. The dynamic price function ( 9) is widely used in the smart grid literature and can be used to encourage users to shift their peak energy demand to non-peak hours [5], [18], [19].…”

“…• We prove that the Stackelberg game has a unique pure strategy Stackelberg equilibrium at which the CES provider maximizes revenue and the users minimize personal energy costs at a unique Nash equilibrium. Non-cooperative game theory has been exploited to develop decentralized DSM frameworks that maximize economic benefits to individual users by coordinating ESSs while satisfying the network voltage constraints [4], [5]. For instance, a DSM framework to maximize per-user economic benefits by coordinating user-owned ESSs has been studied in [5] by developing a voltage-constrained game among the users.…”

“…Non-cooperative game theory has been exploited to develop decentralized DSM frameworks that maximize economic benefits to individual users by coordinating ESSs while satisfying the network voltage constraints [4], [5]. For instance, a DSM framework to maximize per-user economic benefits by coordinating user-owned ESSs has been studied in [5] by developing a voltage-constrained game among the users. In this context, to the best of our knowledge, this paper is the first to leverage Stackelberg game theory with a branch power flow model to study a decentralized energy trading framework between a CES system and users with PV generation for DSM in a neighborhood while satisfying the network voltage constraints.…”

Reinforcement Learning Based Optimal Battery Control Under Cycle-based Degradation Cost

“…The optimal battery control (OBC) problem for determining the (dis)charging policies has been popularly considered to reduce a combination of certain operational costs, such as the net cost for electricity usage, frequency regulation (FR) penalty, and peak-shaving cost; see e.g., [5]- [7]. Additional problem consideration includes the precise circuit model of battery [8] or the network constraints on grid voltage [9]. Due to the dynamics of inputs such as prices or load demands, the OBC problem is challenged by the unknown uncertainty of future signals, and model-predictive control [10] or stochastic This work has been supported by NSF Grants 1802319 and 1952193.…”

Optimal Voltage Control Strategy for Voltage Regulators in Active Unbalanced Distribution Systems Using Multi-Agents

“…d) Moving average of index S: To ensure a smoothed control action, the time series moving average of index S is calculated using (24). 3) Agent data collection…”