Long‐Term Generation Scheduling Method with Constrained Numbers of Unit Commitment and Fuel Constraints on Thermal Units

Abstract

The long-term generation scheduling in power utilities is aimed at maintaining power supply sufficiency and estimating fuel consumption. Multiperiod constraints, such as the allowable number of unit commitments of thermal units and fuel consumption with a given total amount of fuel, must be taken into appropriate consideration for practical economic scheduling in the long-term scheduling horizon. Due to the large size and complexity of such scheduling problems, it is difficult to obtain solutions collectively in terms of stability and processing time in operational situations. We propose a new scheduling method which consists of "preparative processing" and "detailed optimization processing." The former acts to divide the longterm problem with multiperiod constraints into time units of a week according to quick and simplified scheduling results, and the latter acts to minimize the total operation cost on the basis of those weekly partitioned problems. The processing starts with an optimal scheduling result excluding the aforementioned two multi-period constraints and proceeds to sequentially resolve the violation of each constraint with quantitative consideration of interrelationships between them. This paper describes the validation and effectiveness of the proposed method through the real world example of Chubu

Monthly Unit Commitment Model and Algorithm with Renewable Energy Generation Considering System Reliability

“…Regarding the establishment of the objective functions, most of the traditional UC problems [5,6] mainly consider system economy; that is, the target of monthly UC is the total operating cost of the system. After the market-oriented reforms of the power industry, the objective function deducts the lowest cost of purchasing electricity or the largest total social income reflecting the efficiency of resource allocation (see [7][8][9]). At the same time, with the environmental problems getting more and more attention, the emission of pollutants has become one of the optimization targets in [10].…”