In the context of increasing decentralised electricity generation, this paper evaluates the effect of different regulatory frameworks on the evolution of distribution networks. This problem is addressed by means of agent based modelling in which the interactions between the agents of a distribution network and an environment are described. The consumers and the distribution system operator are the agents, which act in an environment that is composed by a set of rules. For a given environment, we can simulate the evolution of the distribution network by computing the actions of the agents at every time step of a discrete time dynamical system. We assume the electricity consumers are rational agents that may deploy distributed energy installations. The deployment of such installations may alter the remuneration mechanism of the distribution system operator. By modelling this mechanism, we may compute the evolution of the electricity distribution tariff in response to the deployment of distributed generation.
In recent years, the vast penetration of renewable energy sources has introduced a large degree of uncertainty into the power system, thus leading to increased trading activity in the continuous intra-day electricity market. In this paper, we propose an agent-based modeling framework to analyze the behavior and the interactions between renewable energy sources, consumers and thermal power plants in the European Continuous Intra-day (CID) market. Additionally, we propose a novel adaptive trading strategy that can be used by the agents that participate in CID market. The agents learn how to adapt their behavior according to the arrival of new information and how to react to changing market conditions by updating their willingness to trade. A comparative analysis was performed to study the behavior of agents when they adopt the proposed strategy as opposed to other benchmark strategies. The effects of unexpected outages and information asymmetry on the market evolution and the market liquidity were also investigated.
Forecasting imbalance prices is essential for strategic participation in the short-term energy markets. A novel twostep probabilistic approach is proposed, with a particular focus on the Belgian case. The first step consists in computing the net regulation volume state transition probabilities. It is modeled as a matrix computed using historical data. This matrix is then used to infer the imbalance prices, since the net regulation volume can be related to the level of reserves activated and the corresponding marginal prices for each activation level are published by the Belgian Transmission System Operator one day before electricity delivery. This approach is compared to a deterministic model, a multi-layer perceptron, and to a widely used probabilistic technique, Gaussian Processes.
This paper proposes a practical modelling solution to the problem of sharing distributed renewable electricity generation in the context of renewable energy communities. According to this approach, the economic benefits of community members, derived from their participation in the community, are shared by means of repartition keys, which represent the proportion of total local electricity to be allocated, ex-post, to each community member. These keys are computed through a centralised optimisation framework that optimally allocates the electricity generated within the community (i.e., local electricity) among the community members so as to minimise the sum of the electricity bills of all community members.
This paper presents a simulation-based methodology for assessing the impact of employing different distribution system operator's remuneration strategies on the economic sustainability of electrical distribution systems. The proposed methodology accounts for the uncertainties posed by the integration of distributed electricity generation resources, and the roll out of smart meters. The different remuneration strategies analysed in this paper include notably new distribution tariffs based on individual peak power consumption and time-dependent rates that are contingent on the time of energy consumption, both requiring smart meters to work. The distributed electricity generation resources are modelled through an optimisation framework and an investment decision process that gradually deploys household photovoltaic installations depending on their profitability and the electricity charges, including the distribution rates. The impact of the distribution system operator's remuneration strategy is measured by an accurate modelling of the remuneration mechanism of this entity, which can adapt to various distribution tariff designs. We analyse this impact over a discrete time horizon. Our methodology is illustrated with several examples of distribution tariffs including old-based on energy consumption or on per-connection fees-as well as new-based on power consumption or time-of use fees-designs. Finally, we provide a comprehensive sensitivity analysis of the proposed simulation environment to the main parameters of the methodology.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.