Search

This paper addresses the issue of the abolition of annual net metering in Slovenia and compares the electric energy costs for the studied active user after the abolition. The article also provides an exploration of the role played by an aggregator, which serves as a central entity that enables individuals to participate in the electric energy market. An analysis of the case study of an active user was made, where an analysis was made of the measurements of household consumption and photovoltaic plant production for the year 2022. This article presents an economic analysis with and without net metering and an analysis of the aggregator involvement strategies. In addition, a battery energy storage system was also considered in the analysis. An important part of the article is the identification of the flexibility potential for shiftable loads, which enable an aggregator to acquire insight into the energy consumption profile and energy production profile of active users. The following indicators were used to compare the strategies: annual electric energy cost and the indicators including self-sufficiency, self-consumption, and grid dependency. The findings indicate that, even in the absence of annual net metering, the active user can lower their costs for electric energy with the help of an aggregator.

Residential chargers are going to become the standard in the near future. Their operational cycles are closely tied to users’ daily routines, and the power consumption fluctuates between zero and peak levels. These types of installations are particularly challenging for the grid, especially concerning the balance of electricity production and consumption. Using battery storage in conjunction with renewable sources (e.g., photovoltaic power plants) represents a flexible solution for grid stabilization, but it is also associated with additional costs. Nowadays, grid authorities penalize a destabilization of the grid resulting from an increased imbalance between electricity generation and consumption and reward contributions to the system balance. Hence, there is a motivation for larger prosumers to make use of this mechanism to reduce their operational costs by better aligning their energy needs with the grid. This study explores the possibility of utilizing battery storage when it is not needed to fulfil its primary function of supporting charging electric vehicles, to generate some additional profit from providing a counter-imbalance. To test this idea, we develop an optimization model that maximizes the economic profit, considering system imbalance penalties/rewards, photovoltaic production, electric vehicle charging demand, and battery storage utilization. By means of computer simulation, we assess the overall operational costs while varying key installation parameters such as battery capacity and power, the installed power of photovoltaic panels and the prediction model’s accuracy. We identify conditions when counter-imbalance has proven to be a viable way to reduce installation costs. These conditions include temporal distribution of charging demand, electricity prices and photovoltaic production. For the morning time window, with a suitable setting of the installation parameters, the cost reduction reaches up to 14% compared to the situation without counter-imbalance.

For national energy capacity improvement and CO2 emission reductions, Mongolia has focused its attention on grid-connected residential PV systems. Due to the feed-in tariff (FIT), the aggregated residential PV systems are expected to increase with the PV penetration level. Currently, there is no power injection limitation in Mongolia. A new policy for the PV penetration level of residential PV systems needs to be developed. This study analyzed the techno-economic performances of distributed PV-battery systems, considering PV generation, the historical load demand, and the tariff structure. We studied the performances of 40 combinations of PV sizes (2 kW–9 kW) and battery capacities (4.4 kWh, 6.6 kWh, 10 kWh, 12 kWh, and 15 kWh) to find feasible system sizes. The aggregated PV-battery systems in a low-voltage (LV) distribution system located in Ulaanbaatar, Mongolia, are also discussed. The results show that six combinations satisfied the technical and economic requirements. The maximum profit was determined to be an NPV of 1650 USD with a 9-year payback period using combination 3 (6 kW PV and 6.6 kWh battery capacity). Combination 6 (8 kW PV and 15 kWh battery capacity) shows that the energy management strategy for residential houses with battery storage has the potential to increase the installed capacity of PV systems without voltage violence in the LV network. For the distributed PV-battery storage system (BSS), the environmental analysis indicates that CO2 and SO2 emissions were reduced by 3929 t/year and 49 t/year, respectively. The findings obtained from this analysis will be used for power system planning.

In this research, we introduce an artificial gorilla troop optimizer for use in artificial neural networks that manage energy consumption in DC–AC hybrid distribution networks. It is being proposed to implement an energy management system that takes into account distributed generation, load demand, and battery-charge level. Using the profile data, an artificial neural network was trained on the charging and discharging characteristics of an energy storage system under a variety of distribution-network power situations. As an added bonus, the percentage of mistakes was maintained far below 10%. An artificial neural network is used in the proposed energy management system, and it has been taught to operate in the best possible manner by using an optimizer inspired by gorillas called artificial gorilla troops. The artificial gorilla troops optimizer optimize the weights and bias of the neural network based on the power of the distributed generator, the power of the grid, and the reference direct axis current to obtain most suitable energy management system. In order to simulate and evaluate the proposed energy management system, small-scale hybrid DC/AC microgrids have been created and tested. When compared to other systems in the literature, the artificial gorilla troops optimizer enhanced neural network energy management system has been shown to deliver 99.55% efficiency, making it the clear winner.

The efficient use of energy resources is profoundly changing power grid regulation and policy. New forms of power generation coupled with storage and the presence of new, increasingly flexible loads such as electric vehicles enable the development of multi-agent planning systems based on new forms of interaction. For instance, consumers can take advantage of flexibility by interacting directly with the grid or through aggregators that bridge the gap between these end-users and traditional centralised markets. This paper aims to provide insight into the benefits for aggregators and end-users from a financial perspective by proposing a methodology that can be applied to different scenarios. End-users may provide flexibility services related to private vehicle charging stations or battery storage systems. The paper will analyse different remuneration levels for end-users by highlighting the most beneficial scenarios for aggregators and end-users and providing evidence on potential conflict of interests. The numerical results show that some consumers may benefit more from aggregation. This is because if taken individually, consumption habits do not allow the same flexibility when considering clusters of consumers with different behaviour. It is also shown that there are cases in which consumers do not seem to benefit from the presence of intermediate parties. We provide extensive numerical results to gain insight for better decision making.

The increased adoption of intermittent renewable sources in the energy sector has also increased the use of battery storage systems. However, the negative impact which the improper disposal of batteries has on the environment has stirred debates on its sustainability. To ensure the proper disposal of battery waste, there is a need to identify and rank the most preferred battery ‘end-of-life’ handling alternatives. This paper focuses on identifying the most preferred ‘end-of-life’ handling alternatives for batteries using a modified triangular intuitionistic fuzzy aggregating and ranking function (TIFARF) model. To test the proposed modified TIFARF model, opinions from experts in the Nigerian renewable energy sector were collected, and the results show that the most preferred alternative is incineration, with a closeness coefficient of 0.130, while the least preferred alternative is recycling, whose closeness coefficient is 0.112. The results are an indication of a lack of facilities needed for the proper recycling of battery remains after their lifetime; if adequate facilities are available, the opinion of experts may be biased towards other alternatives. Future studies should focus on more battery ‘end-of-life’ handling alternatives and on countries with adequate facilities that can be used to manage batteries at the end of their lifespan.

The combined operation of hybrid wind power and a battery energy storage system can be used to convert cheap valley energy to expensive peak energy, thus improving the economic benefits of wind farms. Considering the peak–valley electricity price, an optimization model of the economic benefits of a combined wind–storage system was developed. A charging/discharging strategy of the battery storage system was proposed to maximize the economic benefits of the combined wind–storage system based on the forecast wind power. The maximal economic benefits were obtained based on scenario analysis, taking into account the wind-power forecast error, and costs associated with the loss of battery life, battery operation, and maintenance. Case simulation results highlight the effectiveness of the proposed model. The results show that the hybrid wind–storage system is not only able to convert cheap electricity in the valley period into expensive electricity in the peak period, thus resulting in higher economic benefits, but can also balance the deviation between actual output and plans for the wind power generator to decrease the loss penalty. The analyzed examples show that, following an increase in the deviation of the forecast wind power, the profit of the combined wind–storage system can increase by up to 45% using the charging/discharging strategy, compared with a wind farm that does not utilize energy storage. In addition, the profit of the combined wind–storage system can increase by up to 16% compared with separate systems, following an increase in the deviation penalty deviation coefficient.

Energy access is critical for health, education and social welfare improvements. In countries like Ethiopia, with a low electrification rate and with the majority of the population located in rural areas, about 76% of primary schools do not have access to electricity. This limits the hours of classes and does not allow the use of basic or modern teaching resources. Off-grid solutions have emerged as potential cost-effective alternatives to electrify rural areas and schools, but the availability of off-grid appliances and the size of the system can lead to different solutions. Therefore, this study proposes a DC microgrid system to supply the electricity demand of a rural school located in Ethiopia, considering load estimation scenarios with standard and high-efficiency appliances. The simulation results show that the designed DC microgrid is a valid option to electrify the rural school under each load and generation scenarios. The system costs were also evaluated, and the high-efficiency appliances option has a 51% lower cost. The study also applies to other sub-Saharan countries in the region with very low energy access rates and abundant solar energy, thus giving a large contribution to achieve the United Nations Sustainable Development Education Goal 4 (SDG 4).

This article presents the Reliability Assessment (RA) of renewable energy interfaced Electrical Distribution System (EDS) considering the electrical loss minimization (ELM). ELM aims at minimizing the detrimental effect of real power and reactive power losses in the EDS. Some techniques, including integration of Renewable Energy Source (RES), network reconfiguration, and expansion planning, have been suggested in the literature for achieving ELM. The optimal RES integration (also referred to as Distributed Generation (DG)) is one of the globally accepted techniques to achieve minimization of electrical losses. Therefore, first, the locations to accommodate these DGs are obtained by implementing two indexes, namely Index-1 for single DG and Index-2 for multiple DGs. Second, a Constriction Factor-based Particle Swarm Optimization (CF-PSO) technique is applied to obtain an optimal sizing(s) of the DGs for achieving the ELM. Third, the RA of the EDS is performed using the optimal location(s) and sizing(s) of the RESs (i.e., Solar photovoltaic (SPV) and Wind Turbine Generator (WTG)). Moreover, a Battery Storage System (BSS) is also incorporated optimally with the RESs to further achieve the ELM and to improve the system’s reliability. The result analysis is performed by considering the power output rating of WTG-GE’s V162-5.6MW (IECS), SPV-Sunpower’s SPR-P5-545-UPP, and BSS-Freqcon’s BESS-3000 (i.e., Battery Energy Storage System 3000), which are provided by the corresponding manufacturers. According to the outcomes of the study, the results are found to be coherent with those obtained using other techniques that are available in the literature. These results are considered for the RA of the EDS. RA is further analyzed considering the uncertainties in reliability data of WTG and SPV, including the failure rate and the repair time. The RA of optimally placed DGs is performed by considering the electrical loss minimization. It is inferred that the reliability of the EDS improves by contemplating suitable reliability data of optimally integrated DGs.

Risk in a power system’s ability to survive imminent disturbances without recourse to low operational cost and non-interruptive energy delivery remains the responsibility of every grid operator. Intermittencies in renewable energy and dynamic load variations influence the quality of power supply. The sudden changes affect the system frequency, compromising the reliability of the system grid; generation response to frequency regulation is momentous in such an incident. Slower response or smaller reserve capacity may cause a power shortage. This paper proposes a novel predictive scheme for a short-term operational reliability evaluation for system operations planning. The proposed method evaluates the operational reliability of system frequency whiles considering high renewable power penetration and energy storage system incorporation. Required energy generations, and other grid parameters, are modelled as stochastic inputs to the framework. We formulate a reliability index as a frequency distribution considering system frequency control dynamics and processes. The IEEE Reliability Test System (RTS) is used to prove the efficacy of the proposed model.

Electric air-conditioning systems driven by electricity from a wind turbine can be defined as wind electric and cooling systems according to the definition of solar-activated air-conditioners. They can potentially contribute to reduce primary energy demand and CO2 emission in the civil sector. In this paper, mini wind turbines are considered coupled with a ground source heat pump in order to serve an office building for air-conditioning and supply the electricity surplus for the pure electric load of the user. Different plant configurations are considered. First of all, assessments with two kinds of wind turbines (5−5.5 kW), vertical and horizontal axis, are performed, also considering the coupling with one and two identical wind generators. Secondly, to better use on-site electricity, a parametric study is proposed taking into account different battery storage system sizes (3.2−9.6 kWh). Finally, the plant is simulated in two locations: Naples and Cagliari. Simulation results demonstrate that the source availability mainly affects the system performance. In Cagliari, the primary energy reduction per kWh of final energy demand (for pure electric load, space heating, and cooling) is equal to 1.24, 54.8% more than in Naples. In addition, the storage system limits the interaction with the power grid, lowering the exported electricity from about 50% to about 27% for Naples and from 63% to 50% for Cagliari. The fraction of the load met by renewable energy accounts for up to 25% for Naples and 48% for Cagliari.

Utility-scale battery storage systems typically consist of multiple smaller units contributing to the overall power dispatch of the system. Herein, the power distribution among these units is analyzed and optimized to operate the system with increased energy efficiency. To improve the real-life storage operation, a holistic system model for battery storage systems has been developed that enables a calculation of the energy efficiency. A utility-scale Second-Life battery storage system with a capacity of 3.3 MWh/3 MW is operated and evaluated in this work. The system is in operation for the provision of primary control reserve in combination with intraday trading for controlling the battery state of charge. The simulation model is parameterized with the system data. Results show that losses in power electronics dominate. An operational strategy improving the energy efficiency through an optimized power flow distribution within the storage system is developed. The power flow distribution strategy is based on the reduction of the power electronics losses at no-load/partial-load by minimizing their in-operation time. The simulation derived power flow distribution strategy is implemented in the real-life storage system. Field-test measurements and analysis prove the functionality of the power flow distribution strategy and reveal the reduction of the energy throughput of the units by 7%, as well as a significant reduction of energy losses in the units by 24%. The cost savings for electricity over the system’s lifetime are approximated to 4.4% of its investment cost.

Under the current energy sector framework of electricity tariff in Malaysia, commercial and industrial customers are required to pay the maximum demand (MD) charge apart from the net consumption charges every month. The maximum demand charge will contribute up to 20% of the electricity bill, and will hence result in commercial and industrial customers focussing on alternative energy supply to minimize the billing cost. This paper aims to review the technical assessment methods of a grid-connected solar photovoltaic (PV)—battery storage system—with respect to maximum demand shaving. An effective battery storage system can provide the extra energy needed during the peak energy consumption periods, as well as when renewable energy (RE) sources go offline. Based on the reviews, maximum demand shaving with good Return-of-Investment (ROI) can be achieved by considering the actual load profile, technical, and economic aspects of the solar PV-battery system and the Malaysian electricity tariff for commercial and industrial customers.

The ever-growing diffusion of renewables as electrical generation sources is forcing the electrical power system to face new and challenging regulation problems to preserve grid stability. Among these, the primary control reserve is reckoned to be one of the most important issues, since the introduction of generators based on renewable energies and interconnected through static converters, if relieved from the primary reserve contribution, reduces both the system inertia and the available power reserve in case of network events involving frequency perturbations. In this scenario, renewable plants such as hydroelectric run-of-river generators could be required to provide the primary control reserve ancillary service. In this paper, the integration between a multi-unit run-of-river power plant and a lithium-ion based battery storage system is investigated, suitably accounting for the ancillary service characteristics as required by present grid codes. The storage system is studied in terms of maximum economic profitability, taking into account its operating constraints. Dynamic simulations are carried out within the DIgSILENT PowerFactory 2016 software environment in order to analyse the plant response in case of network frequency contingencies, comparing the pure hydroelectric plant with the hybrid one, in which the primary reserve is partially or completely supplied by the storage system. Results confirm that the battery storage system response to frequency perturbations is clearly faster and more accurate during the transient phase compared to a traditional plant, since time delays due to hydraulic and mechanical regulations are overpassed. A case study, based on data from an existing hydropower plant and referring to the Italian context in terms of operational constraints and ancillary service remuneration, is presented.

A novel, fully decentralized strategy to coordinate charge operation of electric vehicles is proposed in this paper. Based on stochastic switching control of on-board chargers, this strategy ensures high-efficiency charging, reduces load variations to the grid during charging periods, achieves charge completion with high probability, and accomplishes approximate “valley-filling”. Further improvements on the core strategy, including individualized power management, adaptive strategies, and battery support systems, are introduced to further reduce power fluctuation variances and to guarantee charge completion. Stochastic analysis is performed to establish the main properties of the strategies and to quantitatively show the performance improvements. Compared with the existing decentralized charging strategies, the strategies proposed in this paper can be implemented without any information exchange between grid operators and electric vehicles (EVs), resulting in a communications cost reduction. Additionally, it is shown that by using stochastic charging rules, a grid-supporting battery system with a very small energy capacity can achieve substantial reduction of EV load fluctuations with high confidence. An extensive set of simulations and case studies with real-world data are used to demonstrate the benefits of the proposed strategies.