Centralized and decentralized balance group optimization in electricity markets with demand response

In this paper, the potential of using Demand Response (DR) to minimize balancing energy costs of Balance Groups (BGs) in electricity markets is investigated. Two algorithms are developed based on direct and price-based control concepts, respectively, to control an aggregated pool of office buildings. The direct control algorithm is set up as a centralized Model Predictive Control (MPC) problem yielding an optimal control sequence. This is used as a benchmark for a decentralized price control scheme, which is suboptimal, but still provides a good performance with much lower communication requirements compared to the benchmark. The two approaches are compared using a case study and conclusions regarding their advantages and disadvantages are drawn based on simulation results. The results show that with proper exploitation of the flexibility of office building aggregations significant balancing cost reductions can be achieved with only limited communication which is, in particular, respecting privacy requirements.