Deployment and Capacity Allocation of Distributed Energy Resources: A Case Study of Chungcheongnam-do Province, Considering Regional Characteristics and Constraints

초록

This study proposes a spatial optimization approach for the deployment of distributed energy resources (DER) that reflects regional demand/industry structures and transmission constraints. GIS-based multi-criteria decision analysis (MCDA) generates technology-specific suitability maps, which are combined with a capacity-constrained deployment algorithm that complies with legal/environmental site restrictions and hosting capacity. The case study is Chungcheongnam-do, South Korea, which is characterized by concentrated coal-fired power generation capacity and energy-intensive industries. Seven data layers (resources, power grid, load, GIS, existing power plants, demand, policy expansion/closure) are integrated. Suitability scores integrate resource, power grid proximity, load proximity, and brownfield elements with technology-specific weights, and a binary mask applies to siting rules. Capacity is allocated in order of effective score until the target is achieved. Coal closures and wind additions under South Korea's 11th Basic Plan are treated as exogenous constraints, while unspecified timing for combined heat and power and coal-to-LNG conversions are excluded. The resulting deployment includes 2.0 GW of offshore wind, 1.0 GW of onshore wind, 3.0 GW of industrial PV, 0.6 GW of rooftop PV,3.0GW of battery energy storage systems, 0.5 GW of combined heat and power, and 0.15 GW of waste-to-energy facilities. This provides additional benefits compared to policy standards. Annual CO2 emissions will decrease from approximately 52 Mt to approximately 46 Mt (an additional reduction of approximately 11−12%), the volume-weighted generation-load distance will decrease from ≈61 km to ≈50 km(≈18%), and the capacity share within 30 km of demand centers will increase from ≈1% to ≈15−17%. Despite limitations such as regional scope, proxy congestion indicators, and excluded uncertain projects, this method is generalizable and demonstrates that spatially optimized distributed energy resource deployment can reduce transmission dependency and achieve higher carbon reductions when implementing national plans.

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