Search

In this paper, we propose a framework for optimal energy scheduling combined with a transaction mechanism to enable multiple microgrids to exchange their energy surplus/deficit with others while the distributed networks of microgrids remain secure. Our framework is based on two layers: a distributed network layer and a market layer. In the distributed network layer, we first solve optimal power flow (OPF) using a predictor corrector proximal multiplier algorithm to optimally dispatch diesel generation considering renewable energy and power loss within a microgrid. Then, in the market layer, the agent of microgrid behaves either as a load agent or generator agent so that the auctioneer sets a reasonable transaction price for both agents by using the naive auction-inspired algorithm. Finally, energy surplus/deficit is traded among microgrids at a determined transaction price while the main grid balances the transaction. We implement the proposed mechanism in MATLAB (Matlab Release 15, The MathWorks Inc., Natick, MA, USA) using an optimization solver, CVX. In the case studies, we compare four scenarios depending on whether OPF and/or energy transaction is performed or not. Our results show that the joint consideration of OPF and energy transaction achieves as minimal a cost as the ideal case where all microgrids are combined into a single microgrid (or called grand-microgrid) and OPF is performed. We confirm that, even though microgrids are operated by private owners who are not collaborated, a transaction-based mechanism can mimic the optimal operation of a grand-microgrid in a scalable way. [ABSTRACT FROM AUTHOR]

In this paper, we propose novel techniques to reduce total cost and peak load of factories from a customer point of view. We control energy storage system (ESS) to minimize the total electricity bill under the Korea commercial and industrial (KCI) tariff, which both considers peak load and time of use (ToU). Under the KCI tariff, the average peak load, which is the maximum among all average power consumptions measured every 15 min for the past 12 months, determines the monthly base cost, and thus peak load control is extremely critical. We aim to leverage ESS for both peak load reduction based on load prediction as well as energy arbitrage exploiting ToU. However, load prediction inevitably has uncertainty, which makes ESS operation challenging with KCI tariff. To tackle it, we apply robust optimization to minimize risk in a real environment. Our approach significantly reduces the peak load by 49.9% and the total cost by 10.8% compared to the case that does not consider load uncertainty. In doing this we also consider battery degradation cost and validate the practical use of the proposed techniques. [ABSTRACT FROM AUTHOR]