Local Electricity Market Design Utilizing Dynamic Network Usage Tariff
The new technologies emerging in the energy sector pose new requirements for both the regulation and operation of the electricity grid. Revised tariff structures and the introduction of local markets are two approaches that could tackle the issues resulting from the increasing number of active end-users. However, a smooth transition from the traditional schemes is critical, thus creating the need for architecture that can be implemented in the current circumstances. This paper proposes a local market concept and a corresponding dynamic tariff system, which can be operated parallel to the current retail market. The participants of the market can trade energy peer-to-peer via a platform that allocates proper network charges to all transactions. The calculated tariffs consider the physical effect of the transactions on the grid in terms of nodal voltage deviations, branch current flows, and overall system losses. The proposed method is tested on the IEEE European LV test feeder through market simulations. The results imply that with the proper tuning of DNUT (Dynamic Network Usage Tariff) components, the end-users can realize surplus, while the security of network operation is also ensured.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Peer-to-Peer Energy Markets With Uniform Pricing: A Dynamic Operating Envelope Approach
The recent widespread adoption of rooftop solar backed by battery storage is enabling energy customers to both produce and consume electricity (i.e., prosumers of electricity). To facilitate prosumer participation in the…
FairnessMulti-timescale Trading Strategy for Renewable Power to Ammonia Virtual Power Plant in the Electricity, Hydrogen, and Ammonia Markets
Renewable power to ammonia (RePtA) is a prominent zero-carbon pathway for decarbonization. Due to the imbalance between renewables and production energy demand, the RePtA system relies on the electricity exchange with th…
Modeling and Analysis of Utilizing Cryptocurrency Mining for Demand Flexibility in Electric Energy Systems: A Synthetic Texas Grid Case Study
The electricity sector is facing the dual challenge of supporting increasing level of demand electrification while substantially reducing its carbon footprint. Among electricity demands, the energy consumption of cryptoc…
Assessing Long-Term Electricity Market Design for Ambitious Decarbonization Targets using Multi-Agent Reinforcement Learning
Electricity systems are key to transforming today's society into a carbon-free economy. Long-term electricity market mechanisms, including auctions, support schemes, and other policy instruments, are critical in shaping …
Multi-agent Reinforcement LearningTransfer Learning for Electricity Price Forecasting
Electricity price forecasting is an essential task in all the deregulated markets of the world. The accurate prediction of the day-ahead electricity prices is an active research field and available data from various mark…
Transfer Learning