Modelling and Simulation Approaches for Local Energy Community Integrated Distribution Networks

Due to the absence of studies of local energy communities (LECs) where the grid is represented, it is very difficult to infer implications of increased LEC integration for the distribution grid as well as for the wider society. Therefore, this paper aims to investigate holistic modelling and simulation approaches of LECs. To conduct a quantifiable assessment of different control architectures, LEC types and market frameworks, a flexible and comprehensive LEC modelling and simulation approach is needed. Modelling LECs and the environment they operate in involves a holistic approach consisting of different layers: market, controller, and grid. The controller layer is relevant both for the overall energy management system of the LEC and the controllers of single components in a LEC. In this paper, the different LEC modelling approaches in the reviewed literature are presented, several multilayered concepts for LECs are proposed, and a case study is presented to illustrate a holistic simulation where the different layers interact.Modelling and Simulation Approaches for Local Energy Community Integrated Distribution NetworkspublishedVersio

A systematic review of machine learning techniques related to local energy communities

In recent years, digitalisation has rendered machine learning a key tool for improving processes in several sectors, as in the case of electrical power systems. Machine learning algorithms are data-driven models based on statistical learning theory and employed as a tool to exploit the data generated by the power system and its users. Energy communities are emerging as novel organisations for consumers and prosumers in the distribution grid. These communities may operate differently depending on their objectives and the potential service the community wants to offer to the distribution system operator. This paper presents the conceptualisation of a local energy community on the basis of a review of 25 energy community projects. Furthermore, an extensive literature review of machine learning algorithms for local energy community applications was conducted, and these algorithms were categorised according to forecasting, storage optimisation, energy management systems, power stability and quality, security, and energy transactions. The main algorithms reported in the literature were analysed and classified as supervised, unsupervised, and reinforcement learning algorithms. The findings demonstrate the manner in which supervised learning can provide accurate models for forecasting tasks. Similarly, reinforcement learning presents interesting capabilities in terms of control-related applications.publishedVersio

Modeling competition of virtual power plants via deep learning

Traditionally, models pooling flexible demand and generation units into Virtual Power Plants have been solved via separated approaches, decomposing the problem into parts dedicated to market clearing and separate parts dedicated to managing the state-constraints. The reason for this is the high computational complexity of solving dynamic, i.e. multi-stage, problems under competition. Such approaches have the downside of not adequately modeling the direct competition between these agents over the entire considered time period. This paper approximates the decisions of the players via ‘actor networks’ and the assumptions on future realizations of the uncertainties as ‘critic networks’, approaching the tractability issues of multi-period optimization and market clearing at the same time. Mathematical proof of this solution converging to a Nash equilibrium is provided and supported by case studies on the IEEE 30 and 118 bus systems. Utilizing this approach, the framework is able to cope with high uncertainty spaces extending beyond traditional approximations such as scenario trees. In addition, the paper suggests various possibilities of parallelization of the framework in order to increase computational efficiency. Applying this process allows for parallel solution of all time periods and training the approximations in parallel, a problem previously only solved in succession. © 2020 The Author(s)publishedVersio

A transmission expansion model for dynamic operation of flexible demand

This paper proposes a model to include investments in demand flexibility into traditional transmission expansion problems under uncertainty. To do so, a dynamic power flow model is proposed. The model is solved via applying a value function approximation in form of a neural network on the operational problem, allowing to yield a result for the non-convex investment problem. Additionally, robust sets are applied and linearized to deal with uncertainty and decrease computational complexity. In similar manner, Karush Kuhn Tucker conditions are used to transform a tri-level into a bi-level problem. Case studies for systems of varying complexity show the convergence of the algorithm as well as that flexible resources can be used as a cost-effective substitute for transmission lines in grid expansion.publishedVersio

Dynamic Electricity Market Games – Modeling Competition under Large-scale Storage

Electricity shows similar characteristics to traditional commodity goods which led to the market mechanisms to trade electricity being developed in similar manner to markets for other commodities. Kirchho's laws, however, require a constant equilibrium state, as supply surplus or decit is not physically possible in electricity systems. Thus, flexibility in ramping and startups/shutdowns of generation units is a key characteristic that is topic of vast literature on power systems. Competition models, however, have traditionally been focused on representative points in time. Transitions between those time periods have been either approximated or neglected, in order to reduce model complexity and allow for practical applications. The same goes for decisions in-between those time stages. However, as shown by examples such as the Bellmann equations, future decisions can and often will have implications on current periods. In changing systems with decreasing prices and marginal cost, cost factors associated with discontinuous decisions will grow in importance. In electricity systems, these discontinuous decisions are mostly occupied with intertemporal decisions. Therefore, traditional models from game/equilibrium theory might not be t for these future applications. In this dissertation and the presented publications, a novelty in literature is presented: the state decisions of storing inventory and dispatching units are considered in single-level competitive games. This allows for previously ignored applications, such as assessing the strategic impact of dispatch decisions on market prices and electricity storage. In systems with decreasing shares of peak-units and increasing uncertainty, such models could prove key to assessing functionality of market designs and existence of market power. Various other solution methods for equilibrium models beyond the traditional approach of deriving the Karush-Kuhn-Tucker conditions are described and successfully applied within the work of this dissertation. These include Nikaido-Isoda convergence algorithms and Gröbner basis formulations. Approximation techniques are used, either through analytical approaches or dynamically via metaheuristics. Due to non-convexity in the presented interaction models, traditional views on the characteristics of Nash equilibria are reconsidered and redefined. Accurate mapping of these potential outcomes might prove crucial in practical applications, where the results for individual players might vary depending on the equilibrium solution. Thus, analysis on multiple Nash equilibria was provided, in order to display the characteristics of the problem accurately. Further, various applications were introduced. A focus on reserve markets/ancillary services was chosen based on the assumptions of flexible units being the key players in such. Different, modular methodologies were proposed. This allows for using parts of the model individually and potentially combining them with parts of the other presented models. Due to most large-scale storage being provided by hydropower, a focus on realistic examples from this field was chosen. This also resulted in analyzing the modeling of uncertainty, due to the strong dependency of hydropower on natural forces such as precipitation. Formulation of uncertainty in equilibrium models was chosen to be mainly focused on robust/(weighted) interior point methods. These main findings and contributions are meant to contribute to future research on the topic of non-convex multistage games under storage. Due to the complexity/NP-hardness of the problem, the presented methods - even though well performing - can be considered only a starting point for future studies on the here presented novel problem setup

Market Power in Hydro-Thermal Systems with Marginal Cost Bidding

Traditionally, electricity markets have been designed with the intention of disabling producer side market power or prohibiting exercising it. Nonetheless it can be assumed that players participating in pool markets and aiming to maximize their individual benefits might depart from the optimum in terms of total system welfare. To recognize and analyze such behavior, system operators have a wide range of methods available. In the here presented paper, one of those methods - deriving a supply function equilibrium - is used and nested in a traditional discontinuous Nash game. The result is a case study that shows that marginal cost bidding thermal producers have an incentive to collaborate on scheduling in order to cause similar effects to tacit collusionMarket Power in Hydro-Thermal Systems with Marginal Cost BiddingacceptedVersio

A transmission expansion model for dynamic operation of flexible demand

This paper proposes a model to include investments in demand flexibility into traditional transmission expansion problems under uncertainty. To do so, a dynamic power flow model is proposed. The model is solved via applying a value function approximation in form of a neural network on the operational problem, allowing to yield a result for the non-convex investment problem. Additionally, robust sets are applied and linearized to deal with uncertainty and decrease computational complexity. In similar manner, Karush Kuhn Tucker conditions are used to transform a tri-level into a bi-level problem. Case studies for systems of varying complexity show the convergence of the algorithm as well as that flexible resources can be used as a cost-effective substitute for transmission lines in grid expansion

Stochastic variational inference for probabilistic optimal power flows

This paper applies a generative deep learning model, namely a Variational Autoencoder, on probabilistic optimal power flows. The model utilizes Gaussian approximations in order to adequately represent the distributions of the results of a system under uncertainty. These approximations are realized by applying several techniques from Bayesian deep learning, among them most notably Stochastic Variational Inference. Using the reparameterization trick and batch sampling, the proposed model allows for the training a probabilistic optimal power flow similar to a possibilistic process. The results are shown by application of a reformulation of the Kullback-Leibler divergence, a distance measure of distributions. Not only is the resulting model simple in its appearance, it also shows to perform well and accurate. Furthermore, the paper also explores potential pathways for future research and gives insights for practitioners using such or similar generative models

A data set of a Norwegian energy community

This paper presents a data set designed to represent Norwegian energy communities. As such it includes household consumption data collected from smart meter measurements and divided into consumer groups, appliance consumption data collected from Norwegian households, electric vehicle data regarding charging patterns, simulated photovoltaic power generation data based on temperature and irradiance data sets and wholesale electricity prices. All data sets are further filtered by season, weekday/weekend and time segment, and then fitted to either a normal, exponential or log-normal distribution. The reason for this specific segmentation is the intention to provide a suitable data set for case studies and experiments on energy communities that consider uncertainty, a main challenge to be overcome in the practical implementation of energy community projects. In addition to this filtered version, the previously unpublished raw data sets on household consumption and photovoltaic power generation are also provided