This paper investigates the impact of local capacity investments in energy communities on national decarbonization pathways. This study uses a long-term energy system model (POLES), enhanced by an energy system modeling framework (Backbone), to optimize the national transmission and distribution grids. This model is further enhanced to account for local capacity investments in energy communities. The results show that energy communities could accelerate the integration of renewables and alleviate distribution grid constraints by more closely aligning production and demand geographically. However, overly extensive integration of energy communities also has drawbacks: they alter the residual load that the main system must supply, but not uniformly. This means that the peak demand remains high, whereas demand could be divided by two during low-demand hours. This could lead to suboptimal use of nuclear plants, which are required to meet peak demand but must reduce their production, leading to lower revenues during midday hours. Moreover, the deployment of energy communities requires control and exchanges between power system actors, as large-scale integration of uncoordinated local investments results in higher capacity requirements, particularly for solar (+100%) and batteries (+130%). A moderate development of energy communities could nevertheless be cost-effective, especially if community members align their demand with the renewable energy production.

This research proposes a lightweight and adaptable control strategy for managing smart residential photovoltaic-battery systems, aiming to reduce loads' uncertainty from the distribution grid's perspective. The idea is to minimize deviations from a scheduled power profile at the meter level. The profile can be defined dynamically, thus creating a highly versatile yet simple to define management system, able to fulfill requests from the end-users as well as the grid operator, or other stakeholders from the energy system, like aggregators. Unlike traditional predictive energy management systems that rely on centralized optimization and continuous communication (like demand response), the proposed approach operates autonomously and is implemented directly at the converter level, enabling high-resolution real-time control, with no or minimal communication with a central unit or the grid operator. The controller is based on a proximal policy optimization agent that processes sequential inputs and historical data to inform its decisions. Several neural network architectures and input configurations are tested, some integrating forecasts with different horizons and accuracy levels. The controllers' performance is assessed using daily deviation metrics that quantify both the overall error with commitments and the avoidable portion of the deviation relative to the scheduled profile. In addition, this work is validated in real-time on an OPAL-RT device, demonstrating its feasibility for deployment on embedded converter hardware. The proposed method is benchmarked against reference controls: rule-based (for explicability), optimization-based (for theoretical upper bound), and backcasting, an optimization-based control obtained with a delay of a day. Results demonstrate the capability of the reinforcement learning controller to track high-resolution schedules robustly while operating with minimal or no communication for a central unit and at a lower control level than conventional management systems architectures for smart buildings.

The revenues of battery energy storage systems (BESS) participating simultaneously in different markets such as energy and primary reserve has been widely investigated. In most cases, the system profitability is evaluated with optimization approaches based on historical data for prices and frequency measurements. However, in actual operations, the revenue decreases from such an ideal scenario due to uncertainties and the potential impossibility to fulfill the commitments, which translates into economic penalties. This paper proposes two-stage management strategies of a BESS participating in day-ahead and primary frequency reserve markets. The first stage consists in a day-ahead optimization of the quantities for the energy traded and capacity reserved and is based on simple forecasts. Heuristics strategies are then investigated for the real-time phase, based on actual frequency measurements at 10 seconds. Simulations are performed for data in the French market along 2021 and results obtained show that the proposed management can reach up to 90 % of the theoretical optimum profits obtained with perfect forecasts and optimal control. Especially, the real-time operation limits the penalties due to the impossibility to provide reserve when committed. Lastly, a degradation analysis of the BESS over 10 years shows that ageing remains moderated under 20 %.

Medium and low voltage distribution grids are at the core of the energy transition as they are expected to host a large share of renewables and flexible resources. Their modeling within decarbonization pathways is then of great importance in providing realistic future energy scenarios. This paper investigates different scenarios at the French national scale up to 2050 while varying the electricity demand, renewables installed in both transmission and distribution grids, and the considered flexibility technologies. The methodology relies on coupling a longterm energy model (POLES) and an open-source short-term optimization framework (Backbone). POLES produces long-term decarbonization scenarios, while Backbone enables the optimization of the power system. Technical and financial impacts are studied through ten scenarios regarding produced energy, installed capacities, and investment costs. The results highlight the importance of the load demand modeling assumptions, even raising the question of the feasibility of high-demand scenarios. Also, results show that demand-side flexibility can significantly reduce the requirements in conventional storage technologies (up to 98 %). Distributed flexibilities, such as electric vehicle smart charging, are especially effective. Considering multiple types of distribution grids allows, in the end, to show that installing renewable generation at the transmission or distribution level only moderately influences global costs, with a minor advantage for centralization to limit reverse flows on transformers. The paper concludes with a comparison with other scenarios (drawn from up-to-date literature) and a discussion of the environmental footprint of these scenarios, both in terms of mineral resource consumption (raw materials) and land footprint.

Energy Storage Systems (ESS) operators generate revenues by providing electricity products, i.e., energy, capacity, and/or reserve, possibly participating simultaneously in several markets to stack revenues while playing with the discrepancies between prices and provision constraints. This study compares various storage technologies in terms of first-order expected revenues when participating in the Day-Ahead (DA) and Frequency Containment Reserve (FCR) markets, i.e., focusing on their function rather than detailed modeling. Comparisons are thus investigated with a 1 MW-rated asset and data for the French energy markets from 2018 to 2021. The effects of storage parameters, including round-trip efficiency, self-discharge rate, and energy capacity, are considered. Results highlighted that technologies like pumped hydro, compressed air, lead-acid, and Li-ion batteries are the most suitable for participation in DA markets. The FCR market prioritizes higher efficiencies, and technologies like flywheel, super capacitor, and superconducting magnetic energy storage are the most profitable in that market. Ultimately, batteries are the best suited for services stacking, meeting both the DA and FCR market demands.

This paper investigates the impact of key modeling assumptions for the sizing of a Battery Energy Storage Systems (BESSs) participating in energy and reserve markets. Most of the related studies in the literature assume oversimplifications of the operating conditions when computing the expected BESS revenues at the design stage. These considerations oftentimes consist of (i) constant operating efficiency of the BESS, (ii) neglected profit loss due to uncertainties in the operating phase, and (iii) degradation effects that are usually computed in a posteriori analysis. This paper then proposes to successively assess the impact of those modeling assumptions, deriving from a baseline scenario that embeds oversimplifications. At first, results are analyzed in terms of expected revenue decrease compared to the baseline for different BESS sizes, and with the participation in Day-Ahead (DA) and Frequency Containment Reserve (FCR) markets. The analysis reveals that up to 30 % overestimation (more than 60 % in the worst cases) of the profit along the project lifetime could be done in case where the key modeling assumption are simplified. Finally, a sensitivity analysis conducted with different trade-offs between BESS usage (i.e. degradation) and profits shows that the systems displaying power-to-energy ratios of 1 or 0.5 were the most profitable under the markets investigated.

Energy communities have gained significant interest in recent years as they enable active citizen participation in the energy transition. Most research in energy communities delves into strategies for enhancing sustainability, economic viability, and fairness. However, these strategies’ effectiveness largely depends on each energy community’s specific characteristics, including members types and available assets. This study focuses on understanding the impact of input parameters across different energy community typologies. It examines community size, the percentage of prosumers, and the diversity of members’ power profiles, analyzing 24,000 distinct configurations derived from an initial dataset of 92 load profiles. The study evaluates multiple setups for individual choices of solar photovoltaic systems and energy storage assets. The assessment applies a collective optimal management strategy to compare self-consumption and potential energy bill savings against a baseline where end-users operate individually. The same management strategy is applied consistently across various energy community typologies to demonstrate that the outcomes are primarily determined by the diversity of inputs (i.e., the energy community’s specific characteristics) rather than the energy management approach itself. The results indicate that energy communities with more than 20 members do not experience significant performance enhancements, regardless of operational choices. Additionally, the findings highlight that diversifying member types is more beneficial than oversizing the generation and storage asset capacity. Ultimately, the results exhibit that energy communities with a percentage of only consumers yield favorable outcomes for all members. Optimal configurations are identified when the composition comprises 75% of prosumers with heterogeneous load profiles.

When a portion of the low-voltage distribution network, typically a village, is disconnected from the upstream network, due to a fault or maintenance, it can be re-energized temporarily by the distribution system operator. Usually, if closing of a normally open tie-switch connected to a neighboring feeder is not possible, a diesel generator is conveyed to the islanded grid portion, and used towards temporary energy supply. This solution though can be costly, environmentally detrimental or not available. In this paper, an alternative solution towards temporary energy supply of a islanded grid is presented, relying instead only on local renewable sources, here photovoltaic sources, and a small battery. This work further improves decision-making, by providing valuable information to system operator. Given consumption and generation time-series data, the continuous supply of the islanded grid is estimated, depending on several parameters such at the starting time and the demand response scheme enforced.

The potential of Energy Communities (ECs) to foster local private investment in renewable energy production has been highlighted in various recent studies. Almost all these works assume that all investment decisions are taken at year 1, as well as static EC memberships throughout its lifetime. However, as part of a wider energy system, ECs may see their composition evolve with time as founding members may leave or other end-users may join. This uncertainty on the EC’s dynamic composition induces uncertainty on the real cost savings of its members. From this perspective, this work aims at quantifying the impact of newcomers on the profits from investments decided by founding members. To this end, an initial optimal sizing problem is solved before processing a Monte-Carlo analysis on the ECs composition’s evolution. Results collected on a test case composed of 92 end-users show that founding members can lose up to 25% of their expected savings by welcoming new members if no recourse actions are taken on top of the initial optimal investment.

Long-term planning tools compute investment in energy technologies to reach techno-economic objectives. These tools are typically used to define decarbonization pathways with the corresponding capacities for several types of technologies. However, for most renewable-based resources that are expected to be connected in distribution grids, the typical temporal and geographical resolutions of long-term models are deemed insufficient. They cannot capture the need for dynamic flexibility and/or grid reinforcement in distribution and thus the impact on the final costs or carbon emissions. This paper assesses the integration of distribution grids in long-term energy system planning. A coupling is proposed between the long-term model POLES, which computes the technology capacities to install each year up to 2050, with the dispatch/investment model Backbone, which optimizes the operation and the repartition of investments at finer resolutions. The coupling being computationally demanding, several simplifications of Backbone are investigated to find a trade-off with accuracy. Results show that simplifying the investment options and distribution grid models can achieve a 90 % precision on investment and energy generation compared to baseline optimization, with a computational time divided by 100. Finally, comparative simulations are run with the models coupling up to 2050 for the French power system with and without considering medium voltage distribution grids in addition to the transmission one. In the latter case, minor changes occur in the capacity investments. However, operational constraints increase the power system decarbonization cost by 20 % due to distribution constraints, underlining the necessity of considering finer temporal and geographical resolutions.

When a portion of the low-voltage grid is islanded from the main grid (due to a fault on the upstream grid or maintenance), a temporary re-energization solution is necessary. For environmental purposes and security of supply improvement, distribution system operators are also investigating alternative re-energization solutions to the currently used mobile diesel generator, such as local photovoltaic sources and batteries if available. The black start of such an inverter-based microgrid faces many challenges, especially regarding the limited short-circuit current of small-size residential PV inverters. Since telecommunication systems and load monitoring schemes might not be available during emergencies, the re-energization process needs to be designed based on the natural behavior of loads. This paper investigates how inverter stress can be reduced during the re-energization of households, while not being able to monitor the load. For that, domestic load transient measurements are conducted and assessed as a function of their impact on the stability of the considered microgrid. Then the use of a voltage ramp as a transient mitigation strategy is analyzed based on three load categories. The work is based both on detailed electromagnetic transient (EMT) models and on measurements conducted with real-life loads in an experimental facility.

This paper discusses the implementation of supervised learning (SL) as a straightforward data-driven technique to compute the day-ahead bids of grid-connected battery energy systems (BESS) participating in energy markets. The objective is to implicitly account for price uncertainty in the BESS schedule before assessing the economic performance. The case study is a 10 MW BESS battery participating in the day-ahead market. Physic-Informed and more traditional loss functions and a large set of tuning parameters are compared based on the generated daily revenues. Either the power injected by the BESS or its state of charge is controlled, illustrating a compromise to find between the accuracy and the resilience of the results, once confronted with the high volatility of energy prices. The performance of AI-based controllers is assessed in terms of precision with a theoretical optimum obtained with a "perfect forecast". A reference bidding strategy using "backcasting" as a forecast is also considered. Simulation over the year 2021 with an hourly training data set of the energy prices of 2020 shows that SL models do not necessarily perform better than reference results (with a minimal error of 58 %). However, discussions about their tuning and design choices shed light on the complex implementation process of the selected case study.

Energy tariffs and incentives for renewable energy generation are key to encouraging residential users to engage in local energy production and form renewable energy communities (RECs). However, tariffs highly influence decisions and the benefits obtained by stakeholders. Typically, the retail tariff consists of fixed, time of use, inclined blocks, and/or dynamic fares; nevertheless, the more complex the tariff is, the less understandable it is for end-users and not necessarily the best tariff for all stakeholders. Therefore, this research analyzes the sensitivity of RECs' internal prices while implementing three grid purchasing tariffs (fix, time-of-use, and dynamic). The impact on both stakeholders (aggregator and retailer) is analyzed, as well as their interactions and the benefits for the end-users. Results indicate that, regardless of the internal pricing values, the community manager and the REC members receive optimal benefits when those internal tariffs are equal (e.g., purchasing equal to feeding in price).

This research discusses fairness in energy communities while investigating two types of organizations for operation and cost-sharing. On the one hand, centralized architectures consist of operating community assets in a coordinated manner with a central controller before a community manager shares the overall benefits between the users. Four sharing strategies are investigated and implemented in a monthly post-delivery phase. In contrast, in decentralized architectures, each user operates its assets independently. In such frameworks, the costs/benefits are usually shared among users through market-based mechanisms that rely on users' bids. This work then explores the Pool market and Peer-to-peer transactions to investigate the impact of different bidding from the users' perspective. Ultimately, all the proposed centralized and decentralized approaches (10 in total) are assessed based on economic performances at both users' and community levels. Specific attention is paid to fairness within the community, which is challenging. Three indexes derived from economy and game theory are then considered, along with metrics tailored for energy communities. Results from a seven-user community indicate that the pool market systematically returns considerable savings among decentralized frameworks compared to peer-to-peer markets. More importantly, centralized frameworks systematically yield the most significant bill reduction (16 %) and fairer cost allocation compared to decentralized frameworks.

The research focuses on the control of a residential photovoltaic and battery system, designed to mitigate uncertainties from the distribution grid perspective while following prescheduled power profiles at the residential meter level. Specific attention is paid to reduced communication requirements, contrary to the traditional energy management strategy. This autonomy is achieved here using reinforcement learning, which has proven effective in the dynamic management of energy systems. The controller’s effectiveness is compared to other methods, such as rule-based controls and optimization approaches. In the use case, the objective is to follow in real-time a power profile based on the moving average of the net load over a week. The controller’s decision-making process is driven by a proximal policy optimization agent, which uses sequential temporal inputs such as the target power profile, the state of charge, and the net load data from the previous time step. To enhance the performance of the controller, the research explores the integration of a neural network architecture inspired by image processing techniques. Performance evaluation criteria include the daily energy deviation and the daily avoidable energy deviation from the target profile.

This paper studies the impact of a battery storage system connected to a distribution source substation and participating in the Day-Ahead (DA) or Frequency Containment Reserve (FCR) energy markets. The supply of these products results in charge/discharge power profiles added to the natural profiles of the source substations to which these storage systems are connected. These profiles are uncorrelated with the electrical uses initially present in the connection zone and can therefore lead to unexpected technical constraints. This paper thus quantifies the impact of storage facilities operating on wholesale markets and connected to the distribution grid through a selection of metrics. In particular, the number of tap changes of the on-load tap changer is significantly impacted by the participation of the source-connected storage operator in the DA market, which is less the case with FCR.

In recent years, the importance of PV generation data for distribution system operations has increased. However, some behind-the-meter solar installations are still not registered with the system operator and are not necessarily monitored at a centralized level. This "hidden" generation, therefore, increases the difficulty to operate securely and efficiently the distribution grid. This paper introduces a tool dedicated to the automatic detection of such a generation. It is designed to discriminate the nodes with and without local PV generation and is aimed at high accuracy, without local measurements, thus preserving privacy and increasing security. The tool consists of a neural network coupled with a rule-based classification algorithm, which considers only a very limited volume of data (i.e., node consumption and temperature data). Open-access consumption and solar radiation data are used to feed the simulation of a 14-nodes CIGRE distribution grid used to validate the proposed approach. The implemented solution is tested across all the nodes of the selected grid. The sensitivity of the results is analyzed by the level of PV penetration and the period of observation. The tool can recognize the nodes with a new PV installation with an accuracy of up to 100%, depending on exogenous conditions.

Energy communities (ECs) aggregate users within proximity, which have diverse assets and consumption/generation power profiles. Such a variety of user arrangements significantly influences the benefits expected from the ECs. From a vast pool of EC configurations, this paper investigates their composition regarding users’ profiles impact on collective benefits. To that end, clustering is performed for i) different features to characterize an EC and for ii) the performance metrics of the EC once managed. The paper discusses how the community setup impacts its performance, which enables identifying the most relevant features. Hence, 1000 ECs are formed from 10 users. Additionally, two study cases are tested, one with 100% users with PV and battery (i.e., 100% prosumers) and the second with 50% prosumers. The results suggest that in terms of investment, the photovoltaic installed capacity is a more significant asset than storage capacity.

Traditionally, photovoltaic (PV) systems have been operated using maximum power point tracking algorithms, which force the PV arrays to produce the maximum available power at all times. Nevertheless, distribution system operators are increasingly asking for flexible power point tracking (FPPT) algorithms, which allow the regulation of the PV power to a predefined reference value. FPPTs are difficult to tune and often have non-linear behavior. It complicates the modeling of PV systems for power system stability studies. This paper proposes a simplified model that reproduces the dc-side dynamics of a double-stage FPPT-controlled PV system. In addition to its simple tuning, the key advantage of the proposed model is that it can be easily translated into differential equations, which can be used in stability analyses. The proposed model is validated on a temporal simulation as well as a small-signal stability study.

Integrating renewable energy sources (RES) into island microgrids is usually done to provide a cost-effective electricity supply. The integration process is carried out by scheduling generating unit operations with a unit commitment (UC) scheme to ensure low system operating costs. This article discusses developing a UC optimization method for integrating solar photovoltaic plants in Indonesia’s Eastern Sumba microgrid power system. The scope of this study is the optimization algorithm of the UC, which consists of a priority list (PL) for the UC stage and an economic dispatch (ED) that relies on a genetic algorithm (GA) to minimize total operating costs (TOC). The results show that the PL-GA algorithm performs better than the extended priority list (EPL), and combinations of genetic algorithm and Lagrange, by applying continuous problem dispatch and improved binary GA hourly dispatch to meet ramping constraints. The application of RES incentive programs, such as carbon taxes and incentives for RES generation in calculating the TOC, shows an improvement in the financial feasibility analysis of the internal rate of return (IRR) and net present value (NPV) of actual projects in Indonesia.