# Community Energy Storage Management for Welfare Optimization Using a Markov Decision Process

arXiv: https://arxiv.org/abs/2011.13657 · HTML: https://mlsanity.com/paper/2011.13657/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Optimized Renewable Energy Planning MDP for Socially-Equitable Electricity Coverage in the US](https://mlsanity.com/paper/2509.00008/) (2025)
- [Comparing Socially-Equitable Renewable Energy Budget Allocation MDP Policies in Mature and Emerging Economies](https://mlsanity.com/paper/2607.10201/) (2026)

## Abstract (arXiv)

In this paper, we address an optimal management problem of community energy storage in the real-time electricity market under a stochastic renewable environment. In a real-time electricity market, complete market information may not be assessable for a strategic participant, hence we propose a paradigm that uses partial information including the forecast of real-time prices and slopes of the aggregate supply curve to model the price impact of storage use in the price-maker storage management problem. As a price maker, the community energy storage can not only earn profits through energy arbitrage but also smooth price trajectories and further influence social welfare. We formulate the problem as a finite-horizon Markov decision process that aims to maximize the energy arbitrage and social welfare of the prosumer-based community. The advance of the management scheme is that the optimal policy has a threshold structure. The structure has an analytic form that can guide the energy storage to charge/discharge by comparing its current marginal value and the expected future marginal value. Case studies indicate that welfare-maximizing storage earns more benefits than profit-maximizing storage. The proposed threshold-based algorithm can guarantee optimality and largely decrease the computational complexity of standard stochastic dynamic programming.