Evaluation of Hierarchical, Multi-Agent, Community-Based, Local Energy Markets Based on Key Performance Indicators

Abstract

In recent years, local energy markets (LEMs) have been introduced to empower end-customers within energy communities at the distribution level of the power system, in order to be able to trade their energy locally in a competitive and fair environment. However, there is still some challenge with regard to the most efficient approach in organising the LEMs for the electricity exchange between consumers and prosumers while ensuring that they are responsible for their electricity-related choices, and concerning which LEM model is suitable for which prosumer or consumer type. This paper presents a hierarchical model for the organisation of agent-based local energy markets. According to the proposed model, prosumers and consumers are enabled to transact electricity within the local energy community and with the grid in a coordinated manner to ensure technical and economic benefits for the LEM’s agents. The model is implemented in a software tool called Grid Singularity Exchange (GSyE), and it is verified in a real German energy community case study. The simulation results demonstrate that trading electricity within the LEM offers economic and technical benefits compared to transacting with the up-stream grid. This can further lead to the decarbonization of the power system sector. Furthermore, we propose two models for LEMs consisting of multi-layer and single-layer hierarchical agent-based structures. According to our study, the multi-layer hierarchical model is more profitable for household prosumers as compared to trading within the single-layer hierarchical LEM. However, the single-layer LEM is more be beneficial for industrial prosumers.

Advanced Clustering Approach for Peer-to-Peer Local Energy Markets Considering Prosumers’ Preference Vectors

“…where p b i,j,t is the bought price for buyer i to trade P2P energy with seller j, and p s i,j,t is the sold price for seller j to trade P2P energy with buyer i at time slot t as represent by Eqs. ( 47) and (48), respectively. After P2P matching between seller i and buyer j, the bid and offer are updated by subtracting the matched energy quantity from the submitted bid/offer quantity as represented in ( 49) and (50), respectively.…”

“…The proposed LEM advanced clustering model is developed as a Python code and implemented by integrating with the open-source Grid Singularity Exchange (GSy-E) [35], [48], [49] and bidding agent application programming interface (API) as represented in Fig. 5.…”

Intelligent Bidding Strategies for Prosumers in Local Energy Markets Based on Reinforcement Learning

“…Intelligent agents bidding strategy usually derive their trading price based on some optimization model, algorithms, game theoretic approach and/or learning experience. The authors of [8], proposed a linear bidding/offering strategy for LEM that allows agents to linearly decrease/increase their bid/offer price, respectively, in an LEM time slot. This strategy allows prosumer agents to send multiple bids/offers in a single time slot thereby increasing their chance of being matched in the LEM without increasing the computational power requirements of the agents.…”

“…For this, the market model based on the work of Ref. [8] which allow prosumers and consumers to submit multiple bids and offers at a time step. Hence, each market time step is further divided into market ticks t * t as represented in Eq.…”

“…The proposed LEM reinforcement bidding strategy model was developed as a Python code and implemented in the bidding agent application programming interface (API) of the open-source Grid Singularity Exchange (GSy-E) [8],…”