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In this paper, an approach is presented for the demand-side management of residential loads in the urban areas of Pakistan using a battery storage system at the feeder level. The proposed storage system will be installed by a private distributor to supply affordable electricity during peak hours. The experimental data used to carry out this research work are the Pakistan Residential Energy Consumption (PRECON) data set. The households of the data set are categorized based on electric power usage through K-means clustering. The clusters are expanded for feeder synthesis to represent small-scale, medium-scale, and large-scale consumption. This expansion is performed through uniform distribution in a Monte Carlo simulation. The techno-economic analysis for the installation of a battery storage system is carried out for each feeder using SAM. The results of the research project elucidated that the load factors of the feeders representing small-scale, medium-scale, and large-scale consumption improved by 1%, 6%, and 7% by using the optimally sized batteries of 50 kW (670 kWh), 90 kW (1207 kWh), and 100 kW (1360 kWh), respectively. The distributor profit and the consumer utility bill savings ranged from US$12 k to US$25 k. The results proved the validity of the used approach to simultaneously reduce the consumer bill, maximize the distributor profit, and improve the feeder load factor. The novelty of this work lies in the location and in the way the system modeling has been performed with limited data. [ABSTRACT FROM AUTHOR]

Because of electricity markets, environmental concerns, transmission constraints, and variable renewable energy sources (VRES), coordinated operation of demand response (DR) and battery energy storage systems (BESS) has become critical. In turn, the optimal coordinated operation of DR and BESS by an entity can affect overall electricity market outcomes and transmission network conditions. The coordinated operation is desirable for the profit-seeking entity, but it may adversely affect the cost and revenues of other market participants or cause system congestion. Though few coordinated operation models already exist, our aim in this research is to provide a novel multi-objective optimization-based methodology for the coordinated operation of DR and BESS to boost market profit. Moreover, another goal is to simultaneously study the combined effects of such coordinated models on transmission networks and electricity markets for the first time. This paper has proposed a new method for coordinated DR and BESS utilization by a load-serving entity (LSE) to increase its profit. Moreover, it has employed agent-based modeling of the electricity systems (AMES) for testing our coordinated DR and BESS method under day-ahead market and transmission system conditions. Simulation results of case studies indicate that the operating costs of all LSEs decreased, and there was as much as 98,260 $/day in cost savings for BESS deploying LSE1. Although revenues of cheaper generation companies (GenCos) decreased, those of expensive GenCos increased or showed mixed trends. For example, GenCo 3 exhibits an 8765 $/day decrease in revenue for 25% BESS capacity, whereas a 6328 $/day increase in revenue for 37.5% BESS capacity. The variance of LMPs, widely used as a risk index, greatly decreased for the LSE utilizing the coordinated methodology, somewhat decreased for other LSEs but increased for cheaper GenCos with no LSE at the local node. Since BESS deployment decisions of an LSE can have system-wide or market-wide consequences, simulation analysis before deployment can help reduce market distortions or system congestions. [ABSTRACT FROM AUTHOR]