Interactive Planning and Operations using Peak Load Pricing in Distribution Systems

TL;DR

This paper introduces a dynamic Peak-Load Pricing (PLP) method within the DyMonDS framework to optimize distribution system investments by balancing DER deployment, capacity utilization, and social welfare, addressing computational challenges.

Contribution

It presents a novel dynamic PLP methodology integrated with DyMonDS for interactive planning, capable of accurately valuing technology impacts and guiding investment decisions in distribution systems.

Findings

Accurately values the impact of technologies on congestion reduction.

Identifies optimal technologies for each investment cycle.

Enhances decision-making in distribution system planning.

Abstract

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.