With the integration of distributed renewable energy sources (DRES) and active demand (e.g., units providing demand response, DR) in the distribution grid, the importance of monitoring the network conditions, managing the line congestions and observing the voltage levels is increasing. The distribution system operator (DSO) needs a mechanism, such as the traffic light system, to screen and approve the proposed operation schedules of the flexible active resources in the distribution grid. Their aggregated control will require the aggregators to employ advanced scheduling algorithms. The DR scheduling algorithms can be set to pursue various goals, for example, maximization of profit or cost reduction, grid support, or provision of the ancillary services. In the paper, we present a new DR scheduling approach suitable for the aggregation agent using approximate Q‐learning (AQL) algorithm scheduling. We present the AQL algorithm and the associated assumptions used in simulations on a real‐world low‐voltage (LV) grid model, comparing the AQL approach results to those of the economic scheduling and the energy scheduling approaches. Our assumption was that the AQL approach could outperform the energy or economic approaches as the AQL agent would be able to learn to avoid the scheduling penalties. The results of our research show that the aggregator agent using the economic approach shows the best economic performance, but causes the most schedule violations. The energy scheduling approach improves the network voltage profile but lowers the aggregator's profit. The AQL approach results in the agent's economic performance between the former two approaches with minimal schedule violations, confirming our research hypothesis.
This article is categorized under:
Concentrating Solar Power > Systems and Infrastructure
Energy Systems Economics > Systems and Infrastructure
Energy Infrastructure > Systems and Infrastructure
Energy Efficiency > Economics and Policy
The increasing rate of the smart technology implementations in the energy sector brings new control solutions for distributed renewable energy sources (DRES) to tackle the additional challenges on the distribution network that arise from increased integration of renewable energy sources (RES). With these new control solutions, a possibility of new services that could be offered on electricity and ancillary markets emerged, providing a possibility of new source of income for DRES, Demand Response (DR) and Aggregators. This paper presents the main results of the EU FP7 project INCREASE, where innovative controls for DRES and DR units were developed and investigated. The main novelty of the paper is a sensitivity analysis of INCREASE ancillary services and business model applied to representative European grid for overall policy conclusions. The knowledge gained in INCREASE provides the basis for the work in H2020 project STORY, where the demand response strategies will be augmented to encompass small-scale storage solutions.
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