The emergence of Distributed Energy Resources (DERs) provides both challenges and opportunities for the planning and operations of distribution systems. These resources can be deployed in a manner that is either complementary to or in competition with traditional network operations and planning as the DERs can provide numerous important services to grid operators and utilities. This paper presents a novel method to estimate the trade off between DERs and traditional investments using a dynamic Peak-Load Pricing (PLP) methodology. PLP is a pricing strategy for a time-dependent quantity of a non-storable commodity and is based on the theory of long-run marginal costs. Importantly PLP deals with the trade-off between capacity utilization and consumer welfare. Therefore, the capacity price is set at a point where the cost of investment is exactly offset by the additional social welfare that the investment would bring. Importantly it allows for capital cost recovery with no uplift payments. This dynamic PLP methodology is an interactive planning and operations model based on the Dynamic Monitoring and Decision Systems (DyMonDS) Framework which helps to align physical, information, and economic incentives across many stakeholders within the electric energy system. The DyMonDS framework helps to solve the drawbacks of PLP, which are related to the computational complexity of the PLP models considering different technologies with different payback periods over a long investment horizon. Results show that the dynamic PLP model can accurately value the impact of different technologies in reducing congestion and increasing the number of customers served. This allows the network operator to easily identify which technologies should be chosen in each investment cycle.
Real-time Building Energy Storage Scheduling under Electrical Load Uncertainty: A Dynamic Markov Decision Process Approach with Comprehensive Analysis of Different Pricing Policies
Optimizing production scheduling of steel plate hot rolling for economic load dispatch under time-of-use electricity pricing
Xiangtan University（湘潭大学）；Hunan Province Cooperative Innovation Center for Wind Power Equipment and Energy Conversion, College of Information Engineering, Xiangtan University（湘潭大学信息工程学院风力发电设备与能量转换湖南省协同创新中心）；Xiangtan Iron and Steel Corporation Ltd（湘潭钢铁集团有限公司）
Dynamic Pricing in Smart Grids under Thresholding Policies: Algorithms and Heuristics
Masdar Institute of Science and Technology（马斯达尔理工学院）；Brigham Young University（杨百翰大学）；New York University, Abu Dhabi（纽约大学阿布扎比分校）；Massachusetts Institute of Technology（麻省理工学院）
