• Energy Research

The European day-ahead market is characterized by different types of orders and rules, such as block orders and the uniform purchase pricing rule. Block orders are present in many central and northern European countries, whereas the uniform purchase price is enforced in the Italian market under the name of Prezzo Unico Nazionale (PUN). In this paper, we analyze the effects of introducing block orders into the Italian market. In particular, we assess how block orders could affect the computation time, the PUN level, and the number of paradoxically rejected orders. The analysis is performed by varying five main determinants, represented by the total number of blocks, the demanded/offered quantity, the block timespan, the block location, and the demand/supply ratio. Numerical results are obtained by using real Italian market data.

Efficient management of renewable energy resources is imperative for promoting environmental sustainability and optimizing the utilization of clean energy sources. This paper presents a pioneering European-scale study on energy management within renewable energy communities (RECs). With a primary focus on enhancing the social welfare of the community, we introduce a reinforcement learning (RL) controller designed to strategically manage Battery Energy Storage Systems (BESSs) and orchestrate energy flows. This research transcends geographical boundaries by conducting an extended analysis of various energy communities and diverse energy markets across Europe, encompassing different regions of Italy. Our methodology involves the implementation of an RL controller, leveraging optimal control theory for training and utilizing only real-time data available at the current time step during the test phase. Through simulations conducted in diverse contexts, we demonstrate the superior performance of our RL agent compared to a state-of-the-art rule-based controller. The agent exhibits remarkable adaptability to various scenarios, consistently surpassing existing rule-based controllers. Notably, we illustrate that our approach aligns with the intricate patterns observed in both Italian and European energy markets, achieving performance levels comparable to an optimal controller assuming perfect theoretical knowledge of future data.

Abstract Energy storage systems have been recently recognized as an effective solution to tackle power imbalances and voltage violations faced by distribution system operators due to the increasing penetration of low carbon technologies. To fully exploit their benefits, optimal sizing of these devices is a key problem at the planning stage. This paper considers the sizing problem of the energy storage systems installed in a distribution network with the aim, e.g., of preventing over- and undervoltages. In order to accommodate uncertainty on future realizations of demand and generation, the optimal sizing problem is formulated in a two-stage stochastic framework, where the first stage decision involves the storage sizes, while the second stage problem provides the optimal storage control policy for given demand and generation profiles. By taking a scenario-based approach, the two-stage problem is approximated in the form of a single multi-scenario, multi-period optimal power flow, whose size, however, becomes computationally intractable as the number of scenarios grows. To overcome this issue, the paper presents a procedure to compute upper- and lower bounds to the optimal cost of the approximate problem. Moreover, when the objective is to minimize the total installed storage capacity, an iterative algorithm based on scenario reduction is proposed, which converges to the optimal solution of the approximate problem. The whole procedure is tested on the topology of the IEEE 37-bus test network, considering scenarios of demand and generation which feature over- and undervoltages in the absence of storage devices.

This work fits in the context of community microgrids, where members of a community can exchange energy and services among themselves, without going through the usual channels of the public electricity grid. We introduce and analyze a framework to operate a community microgrid, and to share the resulting revenues and costs among its members. A market-oriented pricing of energy exchanges within the community is obtained by implementing an internal local market based on the marginal pricing scheme. The market aims at maximizing the social welfare of the community, thanks to the more efficient allocation of resources, the reduction of the peak power to be paid, and the increased amount of reserve, achieved at an aggregate level. A community microgrid operator, acting as a benevolent planner, redistributes revenues and costs among the members, in such a way that the solution achieved by each member within the community is not worse than the solution it would achieve by acting individually. In this way, each member is incentivized to participate in the community on a voluntary basis. The overall framework is formulated in the form of a bilevel model, where the lower level problem clears the market, while the upper level problem plays the role of the community microgrid operator. Numerical results obtained on a real test case implemented in Belgium show around 54% cost savings on a yearly scale for the community, as compared to the case when its members act individually. 16 pages, 15 figures

This paper addresses the problem of finding the optimal configuration (number, locations and sizes) of energy storage systems (ESS) in a radial low voltage distribution network with the aim of preventing over- and undervoltages. A heuristic strategy based on voltage sensitivity analysis is proposed to select the most effective locations in the network where to install a given number of ESS, while circumventing the combinatorial nature of the problem. For fixed ESS locations, the multiperiod optimal power flow framework is adopted to formulate the sizing problem, for whose solution convex relaxations based on semidefinite programming are exploited. Uncertainties in the storage sizing decision problem due to stochastic generation and demand, are accounted for by carrying out the optimal sizing over different realizations of the demand and generation profiles, and then taking a worst-case approach to select the ESS sizes. The final choice of the most suitable ESS configuration is done by minimizing a total cost, which takes into account the number of storage devices, their total installed capacity and average network losses. The proposed algorithm is extensively tested on 200 randomly generated radial networks, and successfully applied to a real Italian low voltage network and a modified version of the IEEE 34-bus test feeder.

This paper considers power distribution grids and addresses the problem of finding optimal placement strategies for energy storage systems. The placement problem is, unfortunately, combinatorially complex. A placement strategy with performance guarantees based on submodularity theory and greedy methods is proposed. The greedy method is then compared with a heuristic strategy that relies on voltage sensitivity analysis. The features of both techniques are discussed and illustrated through an application to a test feeder.

In this paper a time series approach is proposed for the analysis of electricity prices in the Italian ancillary services market. The objective is to forecast energy prices for lead times ranging from one hour to one day. Both black-box and grey-box multivariate models are adopted for the analysis of the conditional mean values of the price series. Real data regarding a geographical zone of the Italian electricity system are processed, showing that the adopted models allow for a significant reduction of the prediction error variance with respect to that of standard naive predictors.

In this paper, we address the problem of determining the optimal day-ahead generation profile for a wind power producer by exploiting wind speed forecasts provided by a meteorological service. In the considered framework, the wind power producer is called to take part in the responsibility of system operation by providing day-ahead generation profiles and undergoing penalties in case of deviations from the schedule. Penalties are applied only if the delivered hourly energy deviates from the schedule more than a given relative tolerance. The optimal solution is obtained analytically by formulating and solving a stochastic optimization problem aiming at maximizing the expected profit. The proposed approach consists in exploiting wind speed forecasts to classify the next day into one of several predetermined classes, and then selecting the optimal solution derived for each class. The performance of the bidding strategy is demonstrated using real data from an Italian wind plant and weather forecasts provided by a commercial meteorological service.