Coordination Mechanism for PV Battery Systems with Local Optimizing Energy Management

Abstract

This publication presents a coordination mechanism for neighboring photovoltaic (PV) battery systems with local optimizing energy management (EM). The aim of this coordination is a high degree of self-sufficiency for the neighborhood while maintaining a high individual degree of self-sufficiency and relieving the grid. A financial incentive to increase the energy exchanged within the neighborhood is introduced. The local EM of the individual PV battery system uses model predictive control based on deterministic dynamic programming in order to minimize the individual economic costs and extreme grid power values. By using a coordination algorithm involving a central information processing unit, the neighboring PV battery systems are given information about the sum of the planned consumption and feed-in power profiles of the neighborhood, as well as the neighborhood tariffs. Based on these data, the PV battery systems successively optimize the operation of their batteries until either convergence or a maximum count of iterations is achieved. The operating principle of the distributed EM concept with coordination is demonstrated through a simulation of a residential neighborhood comprising eight households with different load profiles and varying PV peak powers and battery capacities. Its performance is compared with three EM concepts: two distributed concepts without coordination and another one with central optimizing EM representing ideal coordination. The resulting power flow distributions are analyzed, and the benefits and weaknesses of the developed coordination mechanism are discussed based on a number of evaluation criteria.

Minimizing Energy Cost in PV Battery Storage Systems Using Reinforcement Learning

“…A range of variations of the model-predictive EM concept [13] with different optimization methods, such as linear programming [17] or dynamic programming [12,18], and different forecasting methods [19] have been investigated in the literature. Model-predictive EM concepts are widely regarded as state of the art in this field.…”

Hybrid Lithium-Ion Battery Storage Solution with Optimizing Energy Management and Online Condition Monitoring for Multi-use Applications

“…For the evaluation of the simulations, several performance criteria were employed. Because of the system being used for self-consumption, first the degree of self-sufficiency k ss = 100% • P load − P grid ,imp (P load ) (12) and the degree of self-consumption…”

“…Hybrid energy storage systems (HESS) are experiencing increasing importance in stationary [8][9][10][11][12] and mobile applications [12-14, 14, 15] due to their ability to improve efficiency, performance, and component lifetime while reducing system costs compared to single-storage solutions. There have been several research projects involving HESS and multi-use applications.…”

Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques

“…The results were assessed in terms of obtained improvements in the PV self-consumption. In [22], optimizing EMS was used to coordinate a group of residential buildings in a high degree of self-sufficiency, with the introduction of financial incentives. The local EMS of individual PV battery systems uses an MBPC based on deterministic dynamic programming to minimize the individual economic costs and grid electricity demand peaks.…”