The Consumer-Centered Electricity Reliability Enhancement in the Standalone Generation Arena

Abstract

Standalone microgrids (MGs) and distributed generations (DGs) suffer power generation intermittencies. This leads to a frequent deficit in the supply to consumers. This is worsened by the further delay experienced in the transaction process of the generated energy. Critical loads face the greatest risk of the resulting power shortage. Using the blockchain-integrated data envelopment analysis (DEA), this paper proposes a customer-centered approach to minimizing these inherent issues. Consequently, generation capacity reliability is maximized while the energy supply latency is minimized. First, considering the varying generation reliabilities of standalone MGs, a simulation method is presented using the DEA algorithm for identifying and selecting the most reliable MG in real time for supply. That is, the microgrid with the maximum generation capacity. Next, to address MG’s energy supply latency, a blockchain-integrated energy trading platform is proposed (for prosumers’ DGs) to enhance the peer-to-peer electricity transaction experience. Thus, considering the inherent transaction delay in blockchain-integrated energy trading platforms, a DEA algorithm is proposed for determining and adopting the fastest blockchain transaction. Finally, an algorithm is developed to streamline the integration of the two-step enhancements. It was observed that, as the input data (efficiencies of MGs and blockchains) are changing, maximum power delivery and the fastest electricity transaction rate are achieved in the standalone generation arena at minimal cost. This addresses the characteristic supply deficits and delays, thus minimizing the risk of shedding critical loads.

Keywords

• Blockchain transaction
• data envelopment analysis (DEA)
• efficiency assessment
• linear regression
• loss of load expectation (LOLE)
• multi-microgrid (MMG)