Installation Of Energy Storage System Research Articles

Thermal stability and durability of solar salt-based nanofluids in concentrated solar power thermal energy storage: An approach from the effect of diverse metal alloys corrosion

Concentrated Solar Power (CSP) technology has witnessed substantial growth, with forecasts predicting an increase of 3.4 GW between 2019 and 2024. This expansion necessitates the installation of energy storage systems to meet the growing demand. Solar molten salts, specifically a mixture of 60 % NaNO3 and 40 % KNO3, have emerged as the primary thermal energy storage (TES) medium in commercial CSP plants. However, a significant challenge lies in the corrosive nature of molten salt at high temperatures, which poses limitations in TES applications. The literature has explored a promising solution: reducing corrosion rates by incorporating nanoparticles into molten salts, creating nanofluids. To assess the viability of nanofluids for CSP, it is essential to understand how they perform under working conditions, especially regarding their thermal stability and durability. This study presents further evidence of nanofluid interactions with component materials under static working conditions. Specifically, focus on the impact of corrosion products precipitated during corrosion tests on the physical and thermal properties of Solar Salt-based silica dioxide nanofluids. In this research, nanofluids in contact with stainless steel, nickel‑chromium alloy, and carbon steel were examined before and after subjecting them to a 90-day thermal exposure at 500 °C. These findings provide valuable data on key thermo-physical properties during service, contributing to the design of more precise TES systems and enhancing their overall efficiency and effectiveness.

Energy management in residential communities with shared storage based on multi-agent systems: Application to smart grids

The evolution towards smart grids (SGs) is mainly characterized by the integration of renewable energy sources (RESs) throughout the grid. The intermittent nature of these sources necessitated the installation of energy storage systems (ESSs) to improve the efficiency and reliability in the power system. Moreover, the ongoing high price of batteries has encouraged the installation of shared ESSs in residential communities. However, managing the shared ESS and the energy flows in the community is considered a key challenge. In order to handle this issue, we introduce a novel energy management system (EMS), namely Energy Management In residential COmmunities with shared storage based on multi-agent systems (EMICO). It finds the optimal energy trading operations between households, as well as the operations of the shared ESS that minimize the total energy losses. We first propose a new cluster-based architecture for the residential community in which we integrate Internet of Energy (IoE) devices to manage energy flows and find the shortest path to transfer energy with minimal loss from a cluster to the other. Then, we model our energy management problem as a constrained optimization problem and we use Lagrange multiplier method to solve it in a centralized way. In order to preserve households’ privacy, we propose a decentralized approach based on multi-agent systems (MASs) to solve our problem. We test our approach on real data traces obtained from a set of households located in the United Kingdom. Numerical results show that EMICO outperforms a literature approach in terms of energy losses (up to 35.66% of reduction in energy losses), electricity bill (up to 21.21% cheaper), number of exchanged messages (up to 83.81% less messages exchanged), and length of required cables (up to 95.03% less cables required).

Optimization and simulation analysis of the energy-intensity based renewable energy supply obligation regulation

Countries around the world are legislating the obligation to install renewable energy systems (RESs) in buildings to achieve global carbon neutrality. The evaluation of the appropriateness of this obligation is mostly carried out at the early design stage of a project. Policymakers should repeatedly modify and complement the policy to ensure that the institutional evaluation method accurately reflects the performance of the RES that will actually be built. This study proposes a methodology that combines optimization algorithms and dynamic simulation techniques to derive policy complements for the renewable energy installation obligation (REIO) regulation being implemented in Seoul. The methodology involves a process that mathematically models the evaluation basis of REIO and identifies exception outliers from standard based on optimization techniques. Through a case study of Seoul REIO regulation using the proposed methodology, it was found that when incentives for energy storage system installation are either too weak or excessive, it can increase the gap between the institutional evaluation results and simulation evaluation results for the RES energy performance.

A comparison of different metaheuristic optimization algorithms on hydrogen storage-based microgrid sizing

Microgrids (MGs) with a high penetration of renewable energy are becoming increasingly popular, mainly due to the need for a sustainable and environmentally friendly power system. However, the stochastic characteristic of renewable energy sources makes it a considerable challenge when designing a microgrid. Appropriate installation of energy storage systems (ESSs) such as battery and hydrogen storage systems are needed to counter the intermittent nature of energy sources. This study presents a comparison and evaluation of eight different metaheuristic approaches for optimizing the size of a hydrogen storage-based microgrid, with the aims of minimizing the microgrid’s cost and ensuring the ability to regulate the energy flow within the system. In addition, the optimization algorithm considers the power of the photovoltaic (PV) system, electrolyzer, fuel cell, and the capacity of the battery and hydrogen tank as decision variables. Results of numerical simulations proved that, under the above problem framework, the particle swarm optimization algorithm outperforms the rest. The algorithm is able to produce an optimized microgrid with a 25.3% lower annual system cost compared to the worst-performing algorithm. Its ability to escape the local optimum solution is also showcased.

Optimal Configuration of User-Side Energy Storage for Multi-Transformer Integrated Industrial Park Microgrid

Under a two-part tariff, the user-side installation of photovoltaic and energy storage systems can simultaneously lower the electricity charge and demand charge. How to plan the energy storage capacity and location against the backdrop of a fully installed photovoltaic system is a critical element in determining the economic benefits of users. In view of this, we propose an optimal configuration of user-side energy storage for a multi-transformer-integrated industrial park microgrid. First, the objective function of user-side energy storage planning is built with the income and cost of energy storage in the whole life cycle as the core elements. This is conducted by taking into consideration the time-of-use electricity price, demand price, on-grid electricity price, and energy storage operation and maintenance costs. Then, considering the load characteristics and bidirectional energy interaction of different nodes, a user-side decentralized energy storage configuration model is developed for a multi-transformer-integrated industrial park microgrid. Finally, combined with the engineering practice constraints, the configuration model is solved by mixed integer linear programming. The simulation test demonstrates how the proposed model can successfully increase the economic benefits of an industrial park. Electricity and demand costs are reduced by 11.90% and 19.35%, respectively, and the photovoltaic accommodation level is increased by 4.2%, compared to those without the installation of energy storage system.

Optimal Management of Energy Storage Systems for Peak Shaving in a Smart Grid

In this paper, the installation of energy storage systems (EES) and their role in grid peak load shaving in two echelons, their distribution and generation are investigated. First, the optimal placement and capacity of the energy storage is taken into consideration, then, the charge-discharge strategy for this equipment is determined. Here, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to calculate the minimum and maximum load in the network with the presence of energy storage systems. The energy storage systems were utilized in a distribution system with the aid of a peak load shaving approach. Ultimately, the battery charge-discharge is managed at any time during the day, considering the load consumption at each hour. The results depict that the load curve reached a constant state by managing charge-discharge with no significant changes. This shows the significance of such matters in terms of economy and technicality.

Strategic Integration of DG and ESS by using Hybrid Multi Objective Optimization with Wind Dissemination in Distribution Network

The importance of distributed generation (DG) has increased recently as a result of the growth in commercial and industrial loads, which has put more pressure on conventional energy sources and utilities. Alternative power generation methods that can handle the massive load without endangering the environment are therefore urgently needed. The installation of Energy Storage Systems (ESSs) may give a substantial opportunity to enhance the aesthetic appeal of the distribution system. DG is a practical substitute for conventional energy sources, which have drawbacks for both the economics and the environment. It goes without saying that there are situations in which a large amount of land and money are required. However, improper DG location or sizing increases safety issues because to the increasing power loss caused by larger reverse flow from the load to the supply. In order to reduce power losses and maintain voltage stability, the ideal location and dimensions for the DG and ESS in IEEE Radial Distribution systems are evaluated. To accomplish these objectives, a hybrid Shuffled Frog Leap Algorithm (SFLA) and Improved Firefly Algorithm (SFLA-IFFA) is suggested. The SFLA-IFFA produced better results in terms of the ideal location and DG size when compared to other existing algorithms.

Optimal selection of energy storage system sharing schemes in industrial parks considering battery degradation

With the continuous deployment of renewable energy sources, many users in industrial parks have begun to experience a power supply–demand imbalance. Although configuring an energy storage system (ESS) for users is a viable solution to this problem, the currently commonly used single-user, single-ESS mode suffers from low ESS utilization efficiency and unsatisfactory investment costs. ESS sharing refers to the joint investment and operation of a set of ESSs by multiple users, which can effectively improve ESS utilization and economic benefits. In the industrial park environment, ESS sharing has multiple schemes that involve different ESS installation structures and energy-sharing methods. Therefore, this study determines the optimal ESS-sharing scheme in an industrial park through the construction of load optimization model and comparative analysis. Several typical ESS-sharing schemes were first established based on different ESS installation structures and energy-sharing methods for comparative analysis. Then, based on each scheme characteristic, a corresponding load optimization model that minimizes the total electricity cost in the industrial park was constructed, after which a simulation experiment was conducted using real historical data. The results indicated that the centralized ESS scheme resulted in lower electricity costs and greater utilization compared to other ESS-sharing schemes, as its cost savings and utilization could reach approximately 18.10 % and 60.75 %, respectively. Despite the resulting increase in energy losses, increasing the proportion of centralized ESS is the most effective method of improving ESS performance.

Explosion protection for prompt and delayed deflagrations in containerized lithium-ion battery energy storage systems

Explosion hazards can develop when gases evolved during lithium-ion battery energy system thermal runaways accumulate within the confined space of an energy storage system installation. Tests were conducted at the cell, module, unit, and installation scale to characterize these hazards. Three installation level tests show that explosion scenarios can occur as prompt ignitions within seconds of cell gas venting or delayed ignitions where gases ignite after a longer duration of accumulation, especially when fire protection systems are actuated. Deflagration venting and exhaust ventilation system design approaches that can be implemented at the installation level are evaluated using a dataset generated from cell, module, and unit level tests. Data from the installation level tests demonstrate the use and effectiveness of deflagration venting for containerized li-ion battery energy storage systems.

Low-carbon economic planning of integrated electricity-gas energy systems

The energy system carbon reduction is an inevitable trend to deal with the global warming problem. The power industry, being a vital part of energy system, faces severe challenges. To decarbonize the power sectors, the implementation of low-carbon technologies and integration of high penetrated renewable power generation would be an effective solution. Therefore, given the existence of multi-type low-carbon technologies including the flexibility reformation of coal-fired units, construction of gas-fired units and installation of energy storage systems, a low-carbon economic planning model of integrated electricity-gas systems with high penetration of wind generation is proposed. The carbon tax and carbon capture technology are integrated to accelerate carbon reductions. In particular, the strategic planning cases contributed by different scenarios are formulated. The simulation results on an electricity-gas test system demonstrate the feasibility and effectiveness of the proposed model in carbon reduction as well as wind curtailment decrease. Compared with the initial state, the implementations of the proposed three planning strategies can reduce carbon emissions by 9.8%, 32.5% and 9.3%, respectively. Meanwhile, the wind power curtailment ratio is decreased by 63.2%, 38.9%, and 63.7%, respectively. Moreover, a sensitivity analysis of carbon tax price and wind power penetration level are performed to investigate the low-carbon transition of the integrated electricity-gas systems.

수요자원 인센티브를 통한 태양광발전 출력제한 대응 ESS 최적운영스케줄링에 관한 연구

The installation of energy storage system (ESS) is increased to solve the instability of the transmission system due to variable renewable energies such as Photovoltaic power generation (PV) and wind power generation. Meanwhile, recently, PV output is frequently curtailed since PV output produced after the ESS charging is finished causes transmission line congestion. For this congestion, it is common to increase line capacity or allocate the output curtailment for each power plant. However, since the unsettled loss occurs due to the output curtailment, the power plant operator should be given an incentive to induce the output curtailment for a specific time. Therefore, in this paper, the ESS optimal operation scheduling using the demand response transaction mechanism as an incentive is presented. From the perspective of PV operator with ESS, the charge/discharge capacity is determined in units of 15 minutes reflecting the PV curtailment and the demand response trading mechanism through mixed-integer linear programming. Uncertainty in the D-Day actual PV power output is considered as three scenarios such as Day-ahead forecasting PV power output and Day-ahead forecasting PV power output scenario with mean absolute percentage errors. Through optimal operation scheduling of ESS, transmission line overload and system congestion are suppressed by ESS charging and PV operators can secure additional benefits through the demand response trading mechanism without PV curtailment. Namely, total expected benefits can be maximized by the proposed method. As a result, an additional expansion of transmission lines or the introduction of a new incentive system can be avoided by the flexibility of PV operators.

Optimal Energy Management of a Campus Microgrid Considering Financial and Economic Analysis with Demand Response Strategies

An energy management system (EMS) was proposed for a campus microgrid (µG) with the incorporation of renewable energy resources to reduce the operational expenses and costs. Many uncertainties have created problems for microgrids that limit the generation of photovoltaics, causing an upsurge in the energy market prices, where regulating the voltage or frequency is a challenging task among several microgrid systems, and in the present era, it is an extremely important research area. This type of difficulty may be mitigated in the distribution system by utilizing the optimal demand response (DR) planning strategy and a distributed generator (DG). The goal of this article was to present a strategy proposal for the EMS structure for a campus microgrid to reduce the operational costs while increasing the self-consumption from green DGs. For this reason, a real-time-based institutional campus was investigated here, which aimed to get all of its power from the utility grid. In the proposed scenario, solar panels and wind turbines were considered as non-dispatchable DGs, whereas a diesel generator was considered as a dispatchable DG, with the inclusion of an energy storage system (ESS) to deal with solar radiation disruptions and high utility grid running expenses. The resulting linear mathematical problem was validated and plotted in MATLAB with mixed-integer linear programming (MILP). The simulation findings demonstrated that the proposed model of the EMS reduced the grid electricity costs by 38% for the campus microgrid. The environmental effects, economic effects, and the financial comparison of installed capacity of the PV system were also investigated here, and it was discovered that installing 1000 kW and 2000 kW rooftop solar reduced the GHG generation by up to 365.34 kg CO2/day and 700.68 kg CO2/day, respectively. The significant economic and environmental advantages based on the current scenario encourage campus owners to invest in DGs and to implement the installation of energy storage systems with advanced concepts.

An investigation for battery energy storage system installation with renewable energy resources in distribution system by considering residential, commercial and industrial load models

Battery energy storage system technique work as alternative load during low demand situation by storing the excess generation and work as alternative power generation source by discharging the stored generation during peak demand. In this work, a comprehensive assessment is performed for battery energy storage system installation and their capacities selection by utilizing the Photovoltaics (PV) output in radial distribution network by considering commercial, residential and industrial time varying load models. The objective of proposed the work is to reduce annual energy losses, mitigate reverse power flow and overvoltage problem and provide peak power dispatch during peak demand in distribution network. A comparative investigation is performed by considering PV and PV-BESS case for each type of load model. Furthermore, the battery energy storage system (BESS) function developed that decide the time and capacity of charging and discharging in order to manage PV penetration and improve the voltage profile, minimize the daily energy losses and control the reverse power flow in the distribution system without deviating the operational limits. A voltage based sensitivity method is developed at initial step to find out the most prominent node in distribution network for BESS installation. The objective of the problem is resolved by genetic algorithm to find out the optimal solution and to assess the comparison of technical parameters between considered cases. The simulation results on IEEE 69 bus test system show that the proposed methodology for BESS placement decreases the annual energy losses, enhances the PV penetration and reduces the overvoltage and mitigate the reverse power flow. Results of this paper also shows that it is possible to dispatch peak power during the peak load hours for each type of load model.

셀프에너지 밸런싱을 고려한 리튬이온전지용 BMS의 운용알고리즘에 관한 연구

Recently, according to the green growth and carbon-neutral policy of government, installation of ESS(energy storage system) with various functions such as peak shaving, frequency regulation and output stabilization of renewable energy sources is rapidly increasing. However, massive scale property has been damaged due to frequent fire accidents in ESS, which is installed and operated on site, and it is reported that most of the fire accidents are occurred during the rest time after full charging. In particular, since Li-ion batteries for ESS are composed of a combination of numerous cells in series and parallel, self-energy balancing phenomenon can be occurred, which current flows from a cell with a high SOC to a cell with a low SOC when the SOC states of cells connected in parallel are different due to deterioration, and the phenomenon is estimated to be one of the causes of the fire accidents. Therefore, this paper proposes a novel circuit configuration and operation algorithm of BMS to prevent self-energy balancing phenomenon in ESS. And also, this paper presents a modeling of self-energy balancing in Li-ion battery using PSCAD/EMTDC and implements a BMS composed of a battery unit, a charger unit, and a self-energy balancing unit, etc., using Labview S/W based on the proposed modeling. From the simulation and test results based on the proposed modeling and test device, it is found that aged cells may be adversely affected by the self-energy balancing phenomenon, and it is confirmed that the proposed BMS is useful tool to prevent self-energy balancing current for the safety of Li-ion battery.

Energy storage systems implementation and photovoltaic output prediction for cost minimization of a Microgrid

Energy storage system (ESS) has great importance in saving energy in new power systems. Optimum selection of these elements poses a new challenge to improve the energy management and prevent cost increases in the system. Also, renewable energy resources have been increasingly used in microgrids. The uncertainty and variation of renewable distributed generation (DG) affect the performance of power systems. In this paper, ESS implementations and photovoltaic (PV) power prediction are used to improve voltage/power profile of the system and reduce the total cost of the microgrid. The purpose of this paper is the optimal installation of ESSs in a microgrid to minimize the total cost where quantile nearest neighbour forecasting is utilized for PV output power prediction as an efficient approach. Gathering data of the last samples in time duration can be used for an effective prediction of PV output in this method, which can overcome PV uncertainty due to changes in solar irradiation and other parameters. Artificial neural networks combined with multi-layer perceptron and genetic algorithm are used for optimizing the size and location of ESSs in the system. Simulation results show that the proposed method improves the power profile as 14%, 21% and 28%, relatively to the scenarios of optimal ESS installation without PV prediction, using PV prediction but with no optimal ESS implementation and not using PV- no ESS implementation, respectively. Moreover, the accuracy of the proposed prediction method is more than the gradient-descent and RNN methods by about 12% and 5%, respectively, as shown in the simulation results. Also, the cost reduction of proposed method is enhanced as 24% and 31% relatively to the cases of optimal ESS installation without PV prediction and PV prediction without optimal ESS implementation, respectively.

Multi-stage optimization of the installation of Energy Storage Systems in railway electrical infrastructures with nature-inspired optimization algorithms

Installing Energy Storage Systems (ESSs) to improve electrical infrastructures of Direct-current (DC) railway systems implies considerable investments that must be assessed carefully. Therefore, it is often necessary to combine detailed railway simulations and decision support mechanisms. Unfortunately, most examples in the literature deal with this topic applying only a single-stage optimization approach: the whole installation is undertaken in a single step, assuming the total budget is available.This paper presents a comprehensive methodology to assess the gradual deployment of the installations when the budget is split into different time periods. This approach is a common situation in real projects and has not been studied yet in the literature. Most often, this type of multi-stage problem is tackled by optimizing each stage independently. On the contrary, this paper proposes to take decisions considering the global impact of each stage optimization, rendering a more efficient solution.This paper proposes a multi-stage formulation of two nature-inspired optimization algorithms (Genetic and Fireworks) to address the installation of ESSs in a realistic railway line. Results demonstrate the excellent behavior of the proposed multi-stage optimization.

ACT on RE+FLEX: Accelerating Coal Transition Through Repurposing Coal Plants Into Renewable and Flexibility Centers

Decarbonizing the power sector forms a critical part of the global combat against climate change. This requires inter alia retirement of the global coal power plant fleet of 2,100 GW. Although a significant part of this capacity is aging, there are complex issues that need to be addressed including the economic viability of existing coal plants in some countries relative to renewable projects and barriers to exit of coal. We have used detailed power plant level operational cost data for ten developing countries with significant share of coal and compared these with levelized cost of renewables, to demonstrate that competitiveness of coal varies significantly across different geographies. Countries like India where renewable projects have been highly competitive and there is an aging fleet of coal plants many of which are far away from mines, are already highly uncompetitive. On the other hand, countries like South Africa that have relatively inexpensive coal plants, but the average cost of renewable projects have not yet dropped sufficiently (as of 2020), will require special efforts to phase out coal completely beyond plants that have reached, or gone well past their technical life. Accelerated retirement of coal would require a new business model that allows repurposing some of these sites for alternative usage including generation from renewables, conversion of the incumbent generator into a synchronous condenser coupled with a fly wheel to provide reactive power and inertia; and installation of energy storage systems. As a repurposed coal plant for energy related activities can retain part of the workforce, it can also address some of the complex social issues. In order to develop a comprehensive repurposing program at a national level, the process needs to follow a least-cost planning methodology to identify prospective coal plant candidates for repurposing and then undertake a cost-benefit analysis of individual projects. We have demonstrated this methodology using a case study for Morocco.

A Feasibility Study of Frequency Regulation Energy Storage System Installation in a Power Plant

The aim of this work is to analyze and stabilize the power system when connecting an energy storage system (ESS) to replace the traditional power reserve of a power plant. Thus, it is necessary to validate and simulate the power facility protection system using a relay coordination approach. The input feasibility of the generator for the frequency regulation (FR) of the operational ESS is also validated through detailed analysis studies including power flow, short circuit and relay coordination analysis. The case scenarios for ESS installation are categorized based on its operation mode and location in the power system. These studies are carried out on the power system at the peak load condition specified for both grids. With the electrical transient analyzer program (ETAP), an analysis is performed to study the implementation of the ESS in a large, integrated power system to determine which location best fits the installation of ESS considering the load flow, short circuit and relay coordination results in each case scenario. Cost evaluation was performed for the choice of locations under study.

Determining the optimum installation of energy storage systems in railway electrical infrastructures by means of swarm and evolutionary optimization algorithms

The installation of wayside Energy Storage Systems (ESSs) in DC-electrified railway systems is one of the main measures to improve their energy efficiency. They store the excess of regenerated energy produced by the trains during the braking phases and give it back to the system when necessary. Nevertheless, the big cost of the associated installation can make railway operators hesitate about the convenience of the investment. Additionally, the decisions about the configuration of the installation (locations and sizes for the ESSs) are usually based on the previous experience of the railway operators or, at best, in assessments made with simulation tools with low accuracy.This paper proposes a model to optimize the profitability of the investment. Nature-inspired optimization algorithms are applied in combination with a very realistic railway simulator. The flexibility of the nature-inspired optimization algorithms, together with their ability to successfully deal with the computationally-intensive and highly non-linear and non-convex problem posed by the realistic railway simulator, makes them the perfect choice.Three different nature-inspired optimization algorithms have been selected and compared: the Genetic Algorithm (GA) as the main exponent of the evolutionary algorithms, the Particle Swarm Optimization algorithm (PSO) as the main exponent of the swarm algorithms and the Fireworks Algorithm (FA) as another variant of the swarm algorithms. The algorithms have shown an excellent behavior, providing solutions that combine the increase of energy efficiency with a very good profitability of the installation required to obtain that increase.

Implementation of innovative projects using renewable energy sources in the fields of “future economy”

The aim of the article is to study the implementation of innovative projects for the use of renewable energy sources in the “economy of the future”. According to the concept of “green" energy transition, the following areas of "economy of the future" are identified: energy efficient industry, buildings, heat energy; electric transport; circulating economy, waste reduction; support for research and innovation on electricity storage, production and storage of green hydrogen; digitalization and technological changes; renewable energy sources – wind, solar, bioenergy. It is proved that renewable energy sources can meet 80% growth in electricity demand over the next 10 years. By 2025, renewable energy sources will displace coal as the main means of electricity generation. If states adopt more aggressive policies, the role of renewable energy will be even more active in the next five years. It is proved that it is important to create an appropriate market environment to attract large-scale private investment in innovative renewable energy projects. After all, without sufficient investment, networks will be a weak link in the transformation of the electricity sector, which will affect the reliability and security of electricity supply. The transition to renewable energy sources in the general energy supply, including transport and heating, is most active in large cities. To transition the city to the “green” energy, the issues of attracting investment, changing consumer behavior, integration of electricity with heat supply and transport, the state of existing energy infrastructure (electricity, gas, heating networks), distribution of energy consumption between sectors (buildings, mobility) and players in supply (large energy companies, enterprises, cooperatives).
 The research of the basic tendencies of realization of projects of use of renewable energy sources in Ukraine is carried out. To ensure competitive conditions for the production of electricity from alternative energy sources, the introduction of incentive mechanisms and the installation of capacities for the accumulation of electricity at power plants is envisaged. Financial support for renewable energy at the state level is provided in two areas: tax benefits and credit support. Among the tax benefits and mechanisms in world practice are the following: investment tax credit; production tax credit; mechanism of partial or full compensation of interest for the use of loans by industrial companies and individual farms for the installation of energy storage systems; mechanism for exemption from taxation of imported equipment for energy storage systems, etc. However, Ukraine has not yet taken sufficient legislative and diplomatic steps to do so. Important are the problems of balancing the network, defaults and debts to market participants, the restructuring of the “green” tariff.