Coordination of community electricity markets and distribution network operation

Community electricity markets are dedicated markets in which small electricity prosumers can directly trade electricity among themselves. The interest in such markets is growing in recent years as indicated by the increasing number of research studies, pilot projects and commercial implementations of community markets. The large-scale deployment of community markets may contribute to problems in the distribution grid, in combination with the increased electrification of the energy demand. Therefore, a coordinated approach with the DSO’s operational activities is preferred in order to avoid network problems. This paper proposes a method for such coordination that is straightforward, effective and considers the electricity deregulation. The results of the case study demonstrate how flexibility from prosumers can be utilized by the DSO to solve network problems. The coordinated approach has a small negative effect on the community market which can be overcome with adequate remuneration policy

Coordination of community electricity markets and distribution network operation

Community electricity markets are dedicated markets in which small electricity prosumers can directly trade electricity among themselves. The interest in such markets is growing in recent years as indicated by the increasing number of research studies, pilot projects and commercial implementations of community markets. The large-scale deployment of community markets may contribute to problems in the distribution grid, in combination with the increased electrification of the energy demand. Therefore, a coordinated approach with the DSO’s operational activities is preferred in order to avoid network problems. This paper proposes a method for such coordination that is straightforward, effective and considers the electricity deregulation. The results of the case study demonstrate how flexibility from prosumers can be utilized by the DSO to solve network problems. The coordinated approach has a small negative effect on the community market which can be overcome with adequate remuneration policy

Online EV charging controlled by reinforcement learning with experience replay

The extensive penetration of distributed energy resources (DERs), particularly electric vehicles (EVs), creates a huge challenge for the distribution grids due to the limited capacity. An approach for smart charging might alleviate this issue, but most of the optimization algorithms has been developed so far under an assumption of knowing the future, or combining it with complicated forecasting models. In this paper we propose to use reinforcement learning (RL) with replaying past experience to optimally operate an EV charger. We also introduce explorative rewards for better adjusting to environment changes. The reinforcement learning agent controls the charger’s power of consumption to optimize expenses and prevent lines and transformers from being overloaded. The simulations were carried out in the IEEE 13 bus test feeder with the load profile data coming from the residential area. To simulate the real availability of data, an agent is trained with only the transformer current and the local charger’s state, like state of the charge (SOC) and timestamp. Several algorithms, namely Q-learning, SARSA, Dyna-Q and Dyna-Q+ are tested to select the best one to utilize in the stochastic environment and low frequency of data streaming

Introducing user preferences for peer-to-peer electricity trading through stochastic multi-objective optimization

Peer-to-peer electricity markets are dedicated markets that enable the direct participation of small electricity end-users in energy trading activities. They are seen as a promising alternative that can empower end-users and accelerate the energy transition, by researchers, business developers, and legislators. Moreover, they can include environmental, social, or altruistic preferences that are relevant to end-users, in addition to the economic perspective. Such preferences are sometimes included in the modeling of P2P markets in the existing literature, but the assumptions behind them are rarely validated in practice. To investigate the desired attributes and preferences of end-users to participate in P2P markets, an online survey including a discrete choice experiment was conducted in The Netherlands The results of the survey are used to design a P2P electricity market with product differentiation. The participants in the market are residential end-users that are equipped with a home energy management system that can control some of the household appliances and automate the decision-making process for participation in the market. To facilitate this, a multi-objective stochastic optimization model is presented that incorporates results from the discrete choice experiment and real smart-meter measurements. The case study results demonstrate user preferences’ influence on market outcomes.</p

Improving Clustering-Based Forecasting of Aggregated Distribution Transformer Loadings With Gradient Boosting and Feature Selection

Load forecasting is more important than ever to enable new monitor and control functionalities of distribution networks aiming to mitigate the impact of the energy transition. Load forecasting at medium voltage (MV) level is becoming more challenging, because these load profiles become more stochastic due to the increasing penetration of photovoltaic (PV) generation in distribution networks. This work combines medium to low voltage (MV/LV) transformer loadings measured with advanced metering infrastructure (AMI) and machine learning (ML) algorithms to propose a new clustering based day-ahead aggregated load forecasting approach. This four-step approach improves the day-ahead load forecast of a city. First, MV/LV transformer loadings are clustered based on the shape of their load pattern. Second, a gradient boosting algorithm is used to forecast the load of each cluster and calculate the related feature importance. Third, feature selection is applied to improve the forecast accuracy of each cluster. Finally, the day-ahead load forecast of all clusters are aggregated. The case study presented uses 519 measured MV/LV transformer loadings in a city to perform 30 day-ahead load forecasts. Compared against the day-ahead aggregated load forecast without clustering, the average normalized root mean squared error (NRMSE) reduced 12.7 %, the average mean absolute percentage error (MAPE) reduced 18.2 % and the average Pearson Correlation Coefficient (PCC) increased 0.37 %. The 95 % confidence interval of the difference between the average NRMSE, MAPE and PCC without clustering and with the proposed method indicates a statistically significant improvement in accuracy

Stability Analysis of Microgrid Islanding Transients based on Interconnected Dissipative Subsystems

To ensure successful islanding of microgrids after a fault has occurred, the transient stability should be analyzed under a set of expected operating conditions during the design and operation of microgrids. Transient stability analysis is conventionally performed with time-domain analysis which is computationally expensive and does not quantify the stability margin. Energy-based methodologies can determine the stability margin, however existing methodologies require significant simplifications to be applied to the microgrid model. The energy-based stability analysis methodology proposed in this paper enables the analysis of high-dimension nonlinear microgrid systems and quantification of the stability margin within reasonable time. The performance of the methodology is validated by analyzing a case study microgrid and comparing the results to time-domain analysis and to a state-of-the-art methodology proposed in the literature. The results indicate that the proposed methodology has a significantly lower computational burden and similar accuracy compared to existing energy-based methodologies. The methodology is able to improve the probability of stable islanding of the case study microgrid from 74% up to 94% when only optimizing the design, and up to 100% when optimizing design and control actions

Practice-Oriented Optimization of Distribution Network Planning Using Metaheuristic Algorithms

Distribution network operators require more advanced planning tools to deal with the challenges of future network planning. An appropriate planning and optimization tool can identify which option for network extension should be selected from available alternatives. However, many optimization approaches described in the literature are quite theoretical and do not yield results that are practically relevant and feasible. In this paper, a distribution network planning approach is proposed which meets requirements originating from network planning practice to guarantee realistic outcomes. This approach uses a state-of-the-art evolutionary algorithm: Gene-pool Optimal Mixing Evolutionary Algorithm. The performance of this algorithm, as well as the proposed model, is demonstrated using a real-world case study

Review of Recent Developments in Technical Control Approaches for Voltage and Congestion Management in Distribution Networks

The increasing installation of distributed energy resources in residential households is causing frequent voltage and congestion issues in low- and medium-voltage electrical networks. To defer or avoid the costly and complicated grid expansion, technical, pricing-based, and market-based approaches have been proposed in the literature. These approaches can help distribution system operators (DSOs) exploit flexible resources to manage their grids. This study focuses on technical control approaches, which are easier to implement, and provides an up-to-date review of their developments in modeling, solution approaches, and innovative applications facilitating indirect control from DSOs. Challenges and future research directions are also discussed

Strategic bidding of distributed energy resources in coupled local and central markets

This paper explores a revenue maximization problem for distributed energy resources in a local day-ahead and balancing market. The local market creates opportunities for competition among distributed energy resources, however it may also lead to exercising market power. In the day-ahead market, the strategic revenue maximization of the distributed energy resources is modelled through a bi-level optimization. The upper-level in the bi-level optimization is from the strategic distributed energy resource's perspective and the lower-level problem is from the local market operator's perspective. The balancing market (where there is perfect competition) is modelled by the shrinking rolling horizon approach. A wind farm with a storage system is considered as a case study of a strategic distributed energy resource to evaluate its profitability within the proposed revenue maximization problem. The revenue of the wind farm in the local market is compared with the one in a (business-as-usual) centralized market where it cannot exercise market power. Sensitivity analysis regarding the effect of changing the distribution system parameters e.g. the branch resistances and the loads, on the revenue of the wind farm and its bidding behaviour is performed. Moreover, the role of the storage system on the revenue of the wind farm is studied. Results show that an overloaded or weak distribution system will positively influence the strategic position of the wind farm. Finally, it is shown that depending on the existence of market power, a storage system can bring extra revenues for the wind farm, by hedging against its uncertain output