Abstract
A novel scalable and privacy-preserving distributed parallel optimization that allows the participation of large-scale aggregation of prosumers with residential PV-battery systems in the market for the ancillary service (ASM) is proposed in this paper. To consider both reserve capacity and energy, day-ahead and real-time stages in the ASM are considered. A method based on hybrid Variable Neighborhood Search (VNS) and distributed parallel optimization is designed for the day ahead and real-time optimization. Different distributed optimization methods are compared and designed and a new distributed optimization method based on Linear Programming (LP) is proposed that outperforms previous methods based on integer and Quadratic programming (QP). The proposed LP-based optimization can be easily coded up and implemented on microcontrollers and connected to a designed Internet of Things (IoT) based architecture. As confirmed by simulation results, carried out considering different realistic case studies, both day-ahead and real-time proposed optimization methods, by allocating the computational effort among local resources, are highly scalable and fulfil the privacy of prosumers.
Highlights
The non-programmability characteristics of generation systems based on renewable energy sources, such as solar and wind power, together with the expected reduction in installed thermoelectric capacity, may cause problems in managing the electrical systems with a possible increase in grid congestion, being such generation systems located far from consumption centers
The lower use of thermoelectric capacity to the advantage of distributed generation will reduce the adequacy of the electrical systems and, in addition to provide for grid investments and long-term price signals for producers, it will be indispensable to implement the widespread use of digital technologies in smart grids
- Different distributed optimization methods are compared and designed and a new distributed optimization method based on Linear Programming (LP) is designed that outperforms previous methods based on integer and Quadratic programming (QP) [12], [13]
Summary
The non-programmability characteristics of generation systems based on renewable energy sources, such as solar and wind power, together with the expected reduction in installed thermoelectric capacity, may cause problems in managing the electrical systems with a possible increase in grid congestion, being such generation systems located far from consumption centers. The algorithm is fully distributed by using Averaging Consensus Network (ACN) to assess the global variables only making use of local information of each user On these bases, innovative scalable and privacy-preserving optimization methods are proposed in this paper that allow the participation of large-scale aggregation of prosumers with. VOLUME 8, 2020 residential PV-battery systems in the market for the ancillary service (ASM) To consider both reserve capacity and reserve energy, the day-ahead and real-time stages in the ASM are considered, in complete compliance with the current balancing market models in Italy for providing up and downregulation. By accessing to the virtual layer, prosumers can exchange only some privacy-preserving information related to their local optimal solutions and their power flexibility contributions to fulfil the global constraints of the global optimization problem as it will be explained in the following. If the energy is not delivered, an imbalance cost is applied, the aggregator should carry out a realtime optimization based on short term forecasts to not pay the imbalance cost
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