Search Results (26)

The unit commitment (UC) problem aims to reduce the power generation costs of power generation units in the traditional power system structure. However, under the current arrangement, the problem of cutting the cost of producing electricity has turned into an opportunity to boost power generation units’ profits. Emission concerns are now given considerable weight when talking about the performance planning of power generation units, in addition to economic objectives. Because emissions are viewed as a limitation rather than an objective function in the majority of recent research that has been published in the literature, this paper solves the multi-objective profit-based unit commitment (PBUC) problem while taking into account energy storage systems (ESSs) and renewable energy systems (RESs) in the presence of uncertainty sources, such as demand and energy prices, in order to minimize generated emissions and maximize profits by power generation units in the fiercely competitive energy market. Owing to the intricacy of the optimization problem, a novel mutation-based modified version of the shuffled frog leaping algorithm (SFLA) is suggested as a way to get around the PBUC problem’s difficulty. A 10-unit test system is used for the simulation, which is run for a whole day to demonstrate the effectiveness of the suggested approach. The proposed algorithm’s output is compared with the best-known approaches from various references. The simulated results generated by the suggested algorithms and the previously reported algorithms to solve the PBUC problem show that the proposed method is better than other evolutionary methods utilized in this study and prior investigations. For example, the overall profit from the suggested MSFLA is around 4% and 5.5% higher than that from other algorithms like the ICA and Muller methods in the presence and absence of reserve allocation, respectively. Furthermore, the MSFLA emissions value is approximately 2% and 8% lower than the optimum emissions values obtained using the PSO and ICA approaches, respectively. Full article

Under the background of the power system profoundly reforming, hydrogen energy from renewable energy, as an important carrier for constructing a clean, low-carbon, safe and efficient energy system, is a necessary way to realize the objectives of carbon peaking and carbon neutrality. As a strategic energy source, hydrogen plays a significant role in accelerating the clean energy transition and promoting renewable energy. However, the cost and technology are the two main constraints to green hydrogen energy development. Herein, the technological development status and economy of the whole industrial chain for green hydrogen energy “production-storage-transportation-use” are discussed and reviewed. After analysis, the electricity price and equipment cost are key factors to limiting the development of alkaline and proton exchange membrane hydrogen production technology; the quantity, scale and distance of transportation are key to controlling the costs of hydrogen storage and transportation. The application of hydrogen energy is mainly concentrated in the traditional industries. With the gradual upgrading and progress of the top-level design and technology, the application of hydrogen energy mainly including traffic transportation, industrial engineering, energy storage, power to gas and microgrid will show a diversified development trend. And the bottleneck problems and development trends of the hydrogen energy industry chain are also summarized and viewed. Full article

The construction and development of energy storage are crucial areas in the reform of China’s power system. However, one of the key issues hindering energy storage investments is the ambiguity of revenue sources and the inaccurate estimation of returns. In order to facilitate investors’ understanding of revenue sources and returns on investment of energy storage in the existing electricity market, this study has established multiple relevant revenue quantification models. The research methodology employed in this paper consists of three main components: Firstly, we established a revenue model and a cost model for energy storage participation in the electricity market. These models focus on arbitrage revenue, subsidy revenue, auxiliary services revenue, investment cost, operational and maintenance cost, and auxiliary service cost of energy storage. Subsequently, we utilized an enhanced Grey Wolf Optimizer algorithm to solve the optimization problem and maximize revenue, thus obtaining the optimal capacity and revenue scale of energy storage in the electricity market. Finally, we compared the whole-lifecycle ROI of different energy storage options in various scenarios. The evaluation results demonstrate that the difference between peak and off-peak loads impacts the investment demand and charging/discharging depth of energy storage. In addition, the discrepancy between peak and off-peak prices affects the arbitrage return of energy storage. These two factors can serve as criteria for energy storage investors to assess their return expectations. When solely considering economic returns and disregarding technical factors, pumped storage energy storage emerges as the most suitable mechanical energy storage option requiring investment. The main contribution of this study lies in the estimation of the lifecycle investment returns for various energy storage technologies in the Chinese electricity market, thus providing valuable insights for the investment and operational practices of market participants. Full article

At present, the traditional scheduling mode of power grids generally dispatches according to the power generation cost within the safe range. Transmission costs are evenly distributed to customers according to their load ratios. There are no methods for the rational distribution of transmission costs according to the utilization degree of generation and load to transmission network resources. This traditional scheduling mode will render transmission cost distribution unfair, and it is difficult to guide reasonable load distribution in time and space. Therefore, an optimal economic scheduling method for power systems based on the whole-system-cost electricity price is proposed in this paper. For the power generation and the transmission sides, the whole-system-cost electricity price model was constructed according to the power flow tracking method. For the load side, a demand-side response model of users’ responses to electricity price changes was established. Finally, the IEEE 57 node standard model was used to simulate optimal economic scheduling. The results show that the proposed method can guide the rational distribution of power flow. The power flow is shifted moderately from far away to near the power generation center, allowing for the load demand to be guided to meet nearby customers’ demands and preventing the line from blocking, the latter of which is conducive to ensuring the safety of the power grid. Full article

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. Full article

Maritime transportation decarbonization has become a crucial factor in reducing carbon emissions and mitigating climate change. As an industry that historically relies on fossil fuels, in particular, heavy fuel oil, the reinvention of the maritime transportation system is occurring at an unprecedented speed to integrate renewable and green energy, low-/zero- carbon fuels, and green infrastructure to support emission-free shipping. In this paper, a two-stage joint optimization model is proposed to optimize the voyage planning of a ship among multiple ports as well as its onboard energy management during each section of the voyage. More specifically, in the first stage, the arrival time of ships is optimized according to the mission of the ship and the electricity prices offered at each port. In the second stage, the speed of the ship, the dispatch of the onboard diesel engine, and the usage of energy storage systems (ESSs) are optimized based on emission control areas and maritime meteorological conditions. Simulation results have shown that the proposed approach would help ship operators minimize the operating cost over the whole voyage while significantly contributing to carbon emissions reduction. Full article

It is important to understand the features of an integrated renewable energy power system, especially for deregulated systems. The greatest obstacle to assimilating renewable energy generators with the existing electrical system is their unpredictability. Because wind energy is inconsistent, incorporating it into an established power system necessitates more planning. The effects of wind farm (WF) incorporation with fuel cells and a unified power flow controller (UPFC) on electric losses, voltage profile, generating price, and the economics of the system in a deregulated power market are examined in this paper. An impact analysis of integrating wind farms into controlled and uncontrolled situations is conducted. At two randomly selected locations in India, the real-time statistics of the actual wind speed (AWS) and forecasted wind speed (FWS) were merged for this study. The surplus charge rate and deficit charge rate are intended to evaluate the imbalance cost which is arising from the difference between anticipated and true wind speeds to determine the economics of the system. Customers are always trying to find electricity that is reliable, inexpensive, and efficient due to the reconfiguration of the power system. As a consequence, the security limitations of the system may be surpassed or might function beyond the safety limit, which is undesirable. In the last section, heuristic algorithms, such as sequential quadratic programming (SQP), artificial bee colony algorithms (ABC), and moth-flame optimization algorithms (MFO), are employed to analyze economic risk. In the real-time energy market, it also covers how the fuel cells and UPFC are utilized to rectify the WF integration’s deviation. Economic risk evaluation approaches include value-at-risk (VaR) and conditional value-at-risk (CVaR). A modified IEEE 30-bus test system is used throughout the whole project. Full article

With the continuous development of the Energy Internet, the demand for distributed energy storage is increasing. However, industrial and commercial users consume a large amount of electricity and have high requirements for energy quality; therefore, it is necessary to configure distributed energy storage. Based on this, a planning model of industrial and commercial user-side energy storage considering uncertainty and multi-market joint operation is proposed. Firstly, the total cost of the user-side energy storage system in the whole life cycle is taken as the upper-layer objective function, including investment cost, operation, and maintenance cost. The lower layer takes the economy and environment of energy storage operation as the goal, and considers the ancillary service market revenue, demand response constraints, and operational constraints. Secondly, considering the uncertainty of the power market price, and based on the robust optimization theory, the robust transformation is carried out to effectively deal with the impact of uncertain variables on the system operation. Finally, the model is verified in a typical IEEE 30-node system. The results show that the uncertainty of renewable energy will affect the optimal location and capacity of energy storage. From the results of energy storage location, energy storage will be configured in the important transmission nodes and renewable energy power generation access nodes in the power system. Full article

As plug-in electric vehicles (PEVs) become more and more popular, there is a growing interest in the management of their charging power. Many models exist nowadays to manage the charging of plug-in electric vehicles, and it is important that these models are implemented in a better way. This paper investigates a price-driven charging management model in which all plug-in electric vehicles are informed of the charging strategies of neighboring plug-in electric vehicles and adjust their own strategies to minimize the cost, while an aggregator determines the unit price based on overall electricity consumption to coordinate the charging strategies of the plug-in electric vehicles. In this article, we used an asynchronous distributed generalized Nash game algorithm to investigate a charging management model for plug-in electric vehicles in a smart charging station (SCS). In a charging management model, we need to consider constraints on the charge and discharge rates of plug-in electric vehicles, the battery capacity, the amount of charge per plug-in electric vehicle, and the maximum electrical load that the whole system can allow. Meeting the constraints of plug-in electric vehicles and smart charging stations, the model coordinates the charging strategy of each plug-in electric vehicle to ultimately reduce the cost of smart charging stations, which is the cost that the smart charging station should pay to the higher-level power supply facility. To the best of our knowledge, this algorithm used in this paper has not been used to solve this model, and it has better performance than the generalized Nash equilibria (GNE) seeking algorithm originally used for this model, which is called a fast alternating direction multiplier method (Fast-ADMM). In the simulation results, the asynchronous algorithm we used showed a correlation error of 0.0076 at the 713th iteration, compared to 0.0087 for the synchronous algorithm used for comparison, and the cost of the smart charging station was reduced to USD 4800.951 after coordination using the asynchronous algorithm, which was also satisfactory. We used an asynchronous algorithm to better implement a plug-in electric vehicle charging management model; this also demonstrates the potential advantages of using an asynchronous algorithm for solving the charging management model for plug-in electric vehicles. Full article

Energy management and utilization for commercial users is becoming increasingly intelligent and refined, fostering a closer and growing connection with the electricity market. In this paper, a novel energy management optimization theoretical framework for commercial users is proposed based on the hybrid simulation of electricity market bidding. The hybrid simulation model based on Multi-Agent Simulation (MAS) with reinforcement learning and System Dynamic Simulation (SDS) is established to solve the problem using a single simulation method: it cannot adjust the clearing price when considering the whole market; considering the uncertainty of Electric Vehicles (EVs) travel and Lighting Loads (LLs), the multi-objective optimization model of energy management for commercial users is constructed to minimize the total energy cost of commercial users, as well as maximize the lighting comfort of indoor office staff, which compensates for the lack of the single-objective optimization of the power consumption for commercial users. A multi-objective optimization model of energy management for commercial users is established based on the hybrid simulation of electricity market bidding. By running the multi-objective optimization model based on hybrid simulation, the results show that the proposed method can realize the optimization of energy management for commercial users considering electricity market bidding. Full article

Grid-connected PV battery systems for private homes are becoming increasingly popular in many countries, including Sweden. This study aimed to evaluate the techno-economic feasibility of such distributed, grid-connected PV battery systems for single homes at a Swedish holiday location. It was especially of interest to investigate the impact of demand charges, as they are frequently introduced by utilities in Sweden and are also common in popular winter sport regions. Grid-connected PV battery systems were sized and optimized based on their net present cost. Load patterns, incentives, demand tariff structures and electricity price variation were used to study the sensitivity of the obtained results. Grid-connected residential PV battery systems were found to be equally profitable compared to grid-connected PV systems without batteries when demand charges were applied. When the load profiles had peak loads throughout the whole year and the batteries were large enough sized to shave many peaks, grid-connected PV battery systems had slightly higher profitability than grid-connected PV systems without batteries. The total savings also depended on the actual rate of demand charge. The good profitability we found greatly depends on the current state incentives for these systems in the form of tax credits for surplus electricity and investment costs. Removing the tax credit for surplus electricity would reduce the savings generated by a grid-connected PV system without batteries significantly more than for grid-connected PV systems with batteries. Full article

In recent decades, the costs of energy in dairy farming increased mainly due to rising energy prices but also due to increased mechanisation and automatisation. Electric energy in dairy farming is essentially used for milking and milk cooling. However, the energy consumption of automatic milking systems (AMS) depend on many factors (e.g., machine generations, machine configurations and settings, and operative conditions). To evaluate the differences in performance and energy efficiency of AMS, the impact of different technologies within the attachment arm in practical conditions, a detailed quantification of energy consumption was carried out on two consecutive single box automatic milking systems (AMS) of a dairy farm in southern Bavaria (Germany). The AMS equipped with an electrical drive of the attachment arm was more efficient and showed a higher capacity regarding cows in the herd. The replacement of the pneumatic drive with electrical drives leads to higher energy consumptions of the milking robot but reduces the energy consumption of the air compressor. Hence, the energy efficiency of the electric attachment arm showed strong advantages in the energetic efficiency of the whole milking process. Advances of sustainability due to the increased performance are and should be investigated in further research. Full article

Wind and solar energies present a time and space disparity that generally leads to a mismatch between the demand and the supply. To harvest their maximum potentials, one of the main challenges is the storage and transport of these energies. This challenge can be tackled by electrofuels, such as hydrogen, methane, and methanol. They offer three main advantages: compatibility with existing distribution networks or technologies of conversion, economical storage solution for high capacity, and ability to couple sectors (i.e., electricity to transport, to heat, or to industry). However, the level of contribution of electric-energy carriers is unknown. To assess their role in the future, we used whole-energy system modelling (EnergyScope Typical Days) to study the case of Belgium in 2050. This model is multi-energy and multi-sector. It optimises the design of the overall system to minimise its costs and emissions. Such a model relies on many parameters (e.g., price of natural gas, efficiency of heat pump) to represent as closely as possible the future energy system. However, these parameters can be highly uncertain, especially for long-term planning. Consequently, this work uses the polynomial chaos expansion method to integrate a global sensitivity analysis in order to highlight the influence of the parameters on the total cost of the system. The outcome of this analysis points out that, compared to the deterministic cost-optimum situation, the system cost, accounting for uncertainties, becomes higher (+17%) and twice more uncertain at carbon neutrality and that electrofuels are a major contribution to the uncertainty (up to 53% in the variation of the costs) due to their importance in the energy system and their high uncertainties, their higher price, and uncertainty. Full article

The European Union sets targets for the extensive use of renewable energy. Meanwhile, the energy production network is changing and transferring from the classic “producer to consumer” scheme to new operation models, where a small consumer with local renewable energy systems becomes a producer–prosumer, an active energy consumer who is also an energy producer. This study evaluated a potential of Latvian households’ participation in the energy market as prosumers. The analysis was based on an informal prospective extrapolation data evaluation method, based on real historical data from the Central Statistical Bureau of Latvia, annual reports of distribution and transmission system operators, assessments, and the conclusions of relevant experts. In addition, the real performance of a photovoltaic (PV) system was evaluated to get information on the whole year’s energy balance, and to compare it with seasonal electricity price fluctuation. The Latvian electricity transmission system is able to accept about 800 MW of additional new renewable energy source (RES) capacity, so there is a great potential for prosumers. The biggest obstacle for a household’s involvement in the energy market is the lack of support mechanisms and relatively high cost of RES technologies. The results show that with the current dynamics of new microgenerator connections, Latvia will achieve the set goals regarding the involvement of prosumers in the achievement of RES goals only in the next century. In order to attract the public to energy production, the concept of energy community needs to be defined in Latvian legislation, a balanced peer trading mechanism needs to be developed for various RES self-consumption groups willing to sell surplus electricity, and tax policy conditions need to be reviewed for electricity transactions outside the NET (payment system), in order to fully ensure the rights of prosumers. Full article

Urban Photovoltaic (PV) systems can provide large fractions of the residential electric demand at socket parity (i.e., a cost below the household consumer price). This is obtained without necessarily installing electric storage or exploiting tax funded incentives. The benefits of aggregating the electric demand and renewable output of multiple households are known and established; in fact, regulations and pilot energy communities are being implemented worldwide. Financing and managing a shared urban PV system remains an unsolved issue, even when the profitability of the system as a whole is demonstrable. For this reason, an agent-based modelling environment has been developed and is presented in this study. It is assumed that an optimal system (optimized for self-sufficiency) is shared between 48 households in a local grid of a positive energy district. Different scenarios are explored and discussed, each varying in number of owners (agents who own a PV system) and their pricing behaviour. It has been found that a smaller number of investors (i.e., someone refuse to join) provokes an increase of the earnings for the remaining investors (from 8 to 74% of the baseline). Furthermore, the pricing strategy of an agent shows improvement potential without knowledge of the demand of others, and thus it has no privacy violations. Full article