Dynamic Tariff for Day-Ahead Congestion Management in Agent-Based LV Distribution Networks

Abstract

Capacity challenges are becoming more frequent phenomena in residential distribution networks with new forms of loads, distributed renewable energy resources (RES) and price-responsive applications. Different types of demand response programs have been introduced to tackle these challenges through iterative changes in price and/or contractual participations based on incentives. In this research, a dynamic network tariff-based demand response program is proposed to address congestion problems in low-voltage (LV) networks. The formulation takes advantage of the scalable architecture of the agent-based systems that allows local decision making with limited communication. Energy consumption schedules are developed on a day-ahead basis depending on the expected cost of overloading for a number of probable scenarios. The performance of the proposed approach has been tested through simulations in the unbalanced IEEE European LV test feeder. Simulation results reveal up to 82% reduction in congestion on a monthly basis, while maintaining the quality of supply in the network.

Centralized Control of Distribution Networks with High Penetration of Renewable Energies

“…Demand response is usually day-ahead, although some methods are applied in realtime. Day-ahead dynamic tariffs aim to favor demand through a flexible pricing system that is established once the network conditions for the next day are known [46]. Distribution locational marginal pricing is a method like dynamic tariffs, but in this case, the tariff price for each consumer depends on the node where they are connected.…”

Dynamic network tariffs: Current practices, key issues and challenges

“…As an indirect control method, electricity price design based on dynamic network tariffs has attracted extensive attention from researchers and network managers because it can adjust the network power flow by mild-but not mandatory-means and benefit market stakeholders, including consumers, through reasonable planning and management. Aiming at LV networks combined with the interactive relationship between dynamic network tariffs and day-ahead dynamic prices, a demand response program was proposed in [65] and [66] based on an agent framework that allows local decision-making with limited communication, and monthly congestion reductions of up to 94% were achieved in [65] and 82% in [66], respectively. A centralised coordinated market mechanism based on HEMS was proposed in [67], which implements daily power-based network tariffs to manage congestion induced by EVs and HPs.…”

Data-Driven Reliability Evaluation of the Integrated Energy System Considering Optimal Service Restoration

“…To model the power flow in DN, the linear DistFlow model (Yeh et al, 2012) is adopted as (1) by neglecting the line loss and assuming a relatively flat voltage profile. The applicability of the linear DistFlow model has been validated through its wide applications in resolving practical operational issues in DN, such as voltage violations (Zhu and Liu, 2015) and line overloading (Haque et al, 2020).…”