Demand response management method for discrete industrial manufacturing system based on constrained reinforcement learning

• Main Authors: ZHANG XIONGFENG, HONG SEUNG HO
• Format: Patent
• Language: English; Korean
• Published: 23.05.2023
• Subjects: CALCULATING; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTINGELECTRIC POWER; COMPUTING; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER; COUNTING; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES; ELECTRIC DIGITAL DATA PROCESSING; ELECTRICITY; GENERATION; PHYSICS; SYSTEMS FOR STORING ELECTRIC ENERGY; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR

Demand response (DR) has been recognized as an effective method of improving the stability and financial efficiency of a power grid. DR realization for the industrial sector as a primary consumer is urgently needed. The present invention provides a novel industrial price-based DR management approach for an industrial discrete manufacturing system by simultaneously considering energy costs and daily production goals. To achieve this, the discrete manufacturing system is modeled as a constrained Markov decision process (CMDP), and a constrained reinforcement learning (CRL) algorithm is adopted to determine an efficient operation strategy for the discrete manufacturing system. A simulation was performed by using a real lithium-ion battery assembly system to verify the performance of the method in accordance with the present invention. The evaluation results showed that the method in accordance with the present invention can optimize energy costs without violating production goals. 수요반응(DR)은 전력망의 안정성과 재정적 효율성을 향상시키는 효과적인 방법으로 인정받고 있다. 1차 소비자로서 산업 부문에 대한 DR 구현이 시급하다. 본 발명은 에너지 비용과 일일 생산 목표를 동시에 고려하여 산업용 이산 제조 시스템을 위한 새로운 산업용 가격 기반 DR 관리 접근 방식을 제안한다. 이를 달성하기 위해 이산 제조 시스템은 CMDP(Constrained Markov Decision Process)로 모델링되고, CRL(Constrained Reinforcement Learning) 알고리즘을 채택하여 이산 제조 시스템에 대한 비용 효율적인 운영 전략을 결정한다. 본 발명에 따른 방법의 성능을 검증하기 위해 실제 리튬 이온 배터리 조립 시스템을 사용하여 시뮬레이션을 수행하였고, 그 평가 결과는 본 발명에 따른 방법이 생산 목표를 위반하지 않고 에너지 비용을 최적화할 수 있음을 보여 주었다.