Cost Of Station Research Articles

Optimization of the effective volume of urban sewage pumping station based on the fuzzy optimization method

ABSTRACT To improve the operational economy of sewage pumping stations, this paper combines the total volume of the drainage network and sewage pumping station to optimize the effective volume of the pumping station reservoir to reduce operating costs without overflow. Taking a residential area in Ningbo city as the study area, the drainage system was simulated based on Infoworks ICM to obtain the changes of sewage under different volumes of pumping station reservoirs. By combining the daily electricity costs of the pump station, pump start-stop times, and flood depth, the fuzzy optimization method was applied to optimize the effective volume of sewage pumping station reservoirs, selecting the optimal solution to reduce operating costs. A sensitivity analysis of the three factors, pump flow rate, the total population, and drainage network volume, was conducted, and the results showed that under the optimal scenario, the optimal effective volume of the pumping station reservoir increases with the increase in pump flow rate, drainage network volume, and total population. This study can provide theoretical support for the optimization of drainage pumping station reservoir volume as well as economical operation.

Research on Intelligent Scheduling Strategy for Electric Heavy Trucks Considering Photovoltaic Outputs

Due to the extensive use of fossil fuels, energy conservation and sustainable transportation have become hot topics. Electric vehicles (EVs), renowned for their clean and eco-friendly attributes, have garnered considerable global attention and are progressively being embraced worldwide. However, disorganized EV charging not only reduces charging station efficiency but also threatens power grid stability. In this low-carbon era, photovoltaic storage charging stations offer a solution that accommodates future EV growth. However, due to the significant instability in both the charging load and photovoltaic power generation within charging stations, it is critical to maximize local photovoltaic power consumption and minimize the impact of disorganized EV charging on the power grid. This paper formulates an intelligent scheduling strategy for electric heavy trucks within charging stations based on typical photovoltaic output data. The study focuses on a photovoltaic storage charging station in an industrial zone in Xinjiang. While considering the electricity procurement cost of the charging station, the aim is to minimize fluctuations in the electricity procurement load. A simulation analysis was conducted using MATLAB 2021a software, and the results indicated that, compared to an uncoordinated charging strategy for electric heavy trucks, the proposed strategy reduced electricity procurement costs by CNY 1348.25, decreased load fluctuations by 169.45, and improved the utilization efficiency of photovoltaic energy by 30%. A statistical analysis was also used to support the reduction in electricity procurement costs and load variations. Finally, a sensitivity analysis of the weight factors in the objective function was performed, proving that the proposed strategy effectively reduces electricity procurement costs and improves the utilization efficiency of photovoltaic energy.

KWH Cost Analysis of Energy Storage Power Station Based on Changing Trend of Battery Cost

Energy storage plays a vital role in enhancing the resilience of the power grid. Utilizing typical capacity and power energy storage application scenarios, coupled with industry research data and technical analysis of energy storage, this study calculates the cost of energy storage per kilowatt-hour and the associated mileage cost. The findings indicate that the current cost per kilowatt-hour of electrochemical energy storage ranges from approximately 0.6 to 0.9 yuan/(kW⋅h), revealing a considerable gap between the target cost for widespread application and the range of 0.3 to 0.4 yuan/(kW⋅h). Therefore, the development of energy storage technologies (EST) should prioritize achieving “low cost, long life, high safety, and easy recycling,” taking into account a comprehensive assessment of system manufacturing, system lifespan, system safety, and recycling. This paper delves into the changing trend of battery costs and their impact on kilowatt-hours, presenting strategic suggestions to reduce the kilowatt-hour cost of ESP stations. The research underscores that a continuous reduction in battery costs will contribute to enhancing the economic benefits of ESP stations and provide robust support for the future development of the energy storage industry.

Firefighting Vehicle Dispatch and Route Planning Model Based on Priority Passage Rights

<div>Efficient fire rescue operations in urban environments are critical for saving lives and reducing property damage. By utilizing connected vehicle systems (CVS) for firefighting vehicles planning, we can reduce the response time to fires while lowering the operational costs of fire stations. This research presents an innovative nonlinear mixed-integer programming model to enhance fire rescue operations in urban settings. The model focuses on expediting the movement of firefighting vehicles within intricate traffic networks, effectively tackling the complexities associated with collaborative dispatch decisions and optimal path planning for multiple response units. This method is validated using a small-scale traffic network, providing foundational insights into parameter impacts. A case study in Sioux Falls shows its superiority over traditional “nearest dispatch” methods, optimizing both cost and response time significantly. Sensitivity analyses involving clearance speed, clearance time, minimum rescue force, and fire loss parameters contribute to the enhancement of urban fire rescue operations and the refinement of practical decision support systems.</div>

Flexible resource demand response scheduling strategy under 5G-V2X

Electric vehicles (EV) and 5 G base stations, functioning as flexible load resources, contribute significantly to the provision of ample reserve capacity for the power grid. The emergence of 5 G Vehicle-to-Everything (V2X) technology has fostered a coupling between EV and base stations, further enhancing their roles within the power grid. Building upon this foundation, this paper proposes a scheduling strategy for demand response in flexibility resources under the 5 G-V2X framework. Initially, the dispatchable load of EV is examined in conjunction with the traffic network. Subsequently, we consider the communication power consumption resulting from the connected vehicle model, enabling us to devise path planning and charging navigation strategies for EV. Lastly, we establish a scheduling decision model that aims to minimize both the operation costs of base stations and the variance in grid load. The simulation is validated using real-world road conditions in the primary urban area of a city. The findings illustrate that the proposed dispatching strategy not only achieves a mutually beneficial outcome for EV users and aggregators, without disrupting EV charging and discharging processes, but also enhances the capability of demand response, thereby ensuring reliable grid operation.

Optimal hybrid power plants for electric vehicle charging demand

Transmission constraints, increasing motivations to decarbonize, and concerns over peak electric vehicle (EV) load impacts on local grids have driven electric customers to consider behind-the-meter, hybrid power plant generation and storage at the distributed-grid level for EV charging. In this study, we develop capabilities to optimize hybrid power plant component capacities for EV charging. We then demonstrate these capabilities in a case study for Boulder, Colorado, using public EV charging data as well as wind and solar resource data.Our results show system designs that balance the cost of energy with load-meeting and peak shaving performance. Within the case study, systems designed for wind, solar photovoltaic (PV), and storage resulted in lower cost of energy than those optimized for PV and storage only. This indicates that in areas where wind resource exists, hybrid power plants that include wind, PV, and battery assets can better meet EV charging loads (including peak loads that are prone to overloading local grids) than PV and battery assets alone. Future work to address limitations in this paper include extending cost modeling to include performance losses (e.g., based on operations or weather) and charging station costs to estimate levelized cost of charging, and quantifying uncertainty and error in our aggregation methods for estimating EV charging loads at the hourly timescale.

Selection of an Optimal Frequency for Offshore Wind Farms

Offshore wind power has attracted significant attention due to its high potential, capability for large-scale farms, and high capacity factor. However, it faces high investment costs and issues with subsea power transmission. Conventional high-voltage AC (HVAC) methods are limited by charging current, while high-voltage DC (HVDC) methods suffer from the high cost of power conversion stations. The low-frequency AC (LFAC) method mitigates the charging current through low-frequency operation and can reduce power conversion station costs. This paper aims to identify the economically optimal frequency by comparing the investment costs of LFAC systems at various frequencies. The components of LFAC, including transformers, offshore platforms, and cables, exhibit frequency-dependent characteristics. Lower frequencies result in an increased size and volume of transformers, leading to higher investment costs for offshore platforms. In contrast, cable charging currents and losses are proportional to frequency, causing the total cost to reach a minimum at a specific frequency. To determine the optimal frequency, simulations of investment costs for varying capacities and distances were conducted.

The Cost Reduction Analysis of Green Hydrogen Production from Coal Mine Underground Water for Circular Economy

The novelty of the paper is the analysis of the possibilities of reducing the operating costs of a mine water pumping station in an abandoned coal mine. To meet the energy needs of the pumping station and reduce the carbon footprint, “green” energy from a photovoltaic farm was used. Surplus green energy generated during peak production is stored in the form of green hydrogen from the water electrolysis process. Rainwater and process water are still underutilized sources for increasing water resources and reducing water stress in the European Union. The article presents the possibilities of using these waters, after purification, in the production of green hydrogen by electrolysis. The article also presents three variants that ensure the energy self-sufficiency of the proposed concepts of operation of the pumping station.

Stochastic fast charging scheduling of battery electric buses with energy storage systems design

Under the background of urban green and low-carbon economic development, battery electric buses (BEBs) together with fast charging technologies are considered as an effective way in promoting carbon emissions reduction and improving energy efficiency. However, challenges arise during daily peak periods, at which BEB charging activities cause increased operation costs and substantial stress on the power grid. To fill the gaps, this work introduces energy storage systems (ESSs) into the BEB fast-charging scheduling problem. A stochastic programming model considering uncertain discharge efficiencies of ESSs is established, aiming to minimize total operation costs of fast charging stations. It is then transformed into a deterministic one with a manageable scenario sample size, using an integrated sample average approximation (SAA) and K-means approach, as well as an integrated SAA and K-means++ approach. We also present a heuristic algorithm and an improved genetic algorithm to solve the transformed model. Numerical studies based on a real-life urban transit network in Shanghai, China, are conducted to demonstrate the effectiveness of the proposed methods. Sensitivity analysis further reveals the impact of charging power on total operation cost.

Peak shaving potential and its economic feasibility analysis of V2B mode

Electric vehicles (EVs) as mobile energy-storage devices improve the grid's ability to absorb renewable energy while reducing peak-to-valley load differences. With a focus on smoothing the load curve, this study investigates the peak shaving potential and its economic feasibility analysis of V2B mode. First, based on the virtual microgrid system established in this paper, a Monte Carlo method was used to simulate the driving, charging, and discharging processes of EVs. Second, the effects of the number of charging stations, battery capacity of EVs, and photovoltaic (PV) penetration rate on the peak shaving results were evaluated separately by using the standard deviation of daily loads. Third, the V2B peak shaving strategy and cost composition were explored considering the battery aging costs. Moreover, the economic feasibility and factors influencing the proposed strategy were analyzed. The results indicate that 1) V2B effectively transfers peak power loads between 08:00 h And 20:00 h To low-demand periods at night; 2) as the deployment ratio of charging station increases, the level of load fluctuation decreases significantly. When EV battery capacity is at or above 60 kW h, charging station deployment ratios of 0.43, 0.42, and 0.47 are required for saturated load smoothing in winter, transition period, and summer, respectively; 3) the PV penetration rate for maximizing the collaborative peak shaving capability of V2B and PV power is 30%–40 % and 4) reducing battery costs or extending battery life has the most significant effect on V2B power costs, followed by charging station costs. This study provides a theoretical basis for determining the economic feasibility of charging station planning and provides technical guidance for the rational scheduling of EVs and their friendly interaction with the power grid.

A Model for Electrifying Fire Ambulance Service Stations Considering Practical Service Data and Charging Strategies

Due to concerns with air pollution and climate change, governments and transport operators around the world have engaged in transforming their fossil-fueled vehicles into electric vehicles (EVs). It is essential to build a model for the electrifying process to minimize the operation costs. This paper presents a systematic analytical approach for the electrification of a fire ambulance service station. This approach begins with the selection of suitable EVs to replace the current service vehicles. Subsequently, an in-depth analysis is conducted to determine the practical utilization of EVs at the station. The model proposes two charging strategies: immediate charging upon an EVs’ return and smart charging. Based on the chosen EVs and charging strategies, a comprehensive assessment of the load profiles for the planned EV charging station is performed. In accordance with the load profiles, a mathematical model to minimize the infrastructure and operation costs of the charging station is proposed. Various pricing schemes are compared to identify the most efficient pricing scheme for the charging station, and economic analyses of the EVs and traditional ambulance vehicles are proposed in this paper. The test results indicate that the progressive pricing scheme is well suited for immediate charging strategies, whereas smart charging should opt for the time-of-use pricing scheme. Selecting the appropriate pricing scheme has the potential to significantly reduce electric energy costs.

Innovative optimal risk and economic management of a hybrid electric/hydrogen refueling station with water electrolyze and steam methane reform technologies for hydrogen supply

This paper proposes a stochastic programming for the economic operation of an electric/hydrogen refueling station. To reduce the carbon footprint, the charging station is connected to solar power generation, electrolyzer, and fuel-cell device to interlink the electric and hydrogen energies. Extra hydrogen is procured through an innovative steam methane reform process, which charges additional gas prices and emission penalties in return. The optimal scheduling minimizes the operation cost of the charging station and the emissions produced. The decision-maker faces uncertainties of electricity price, solar power generation, and vehicle demand that are modeled using stochastic programming. The innovative method based on conditional value-at-risk method is proposed to develop risk-hedging strategies addressing financial risks using economic management. With this respect, the risk-aversion strategies increased the expected cost from $ 322.97 to $ 388.71 (about 17 %) and decreased the difference between the expected cost and the cost of the worst scenario from $ 78.04 to $ 12.30 (about 84.23 %). Moreover, expected risk reduced from $ 24.63 to $ 1.23.

Hierarchical Energy Management of DC Microgrid with Photovoltaic Power Generation and Energy Storage for 5G Base Station

For 5G base stations equipped with multiple energy sources, such as energy storage systems (ESSs) and photovoltaic (PV) power generation, energy management is crucial, directly influencing the operational cost. Hence, aiming at increasing the utilization rate of PV power generation and improving the lifetime of the battery, thereby reducing the operating cost of the base station, a hierarchical energy management strategy based on the improved dung beetle optimization (IDBO) algorithm is proposed in this paper. The first control layer provides bus voltage control to each power module. In the second control layer, a dynamic balance control strategy calculates the power of the ESSs using the proportional–integral (PI) controller and distributes power based on the state of charge (SOC) and virtual resistance. The third control layer uses the IDBO algorithm to solve the DC microgrid’s optimization model in order to achieve the minimum daily operational cost goal. Simulation results demonstrate that the proposed IDBO algorithm reduces the daily cost in both scenarios by about 14.64% and 9.49% compared to the baseline method. Finally, the feasibility and effectiveness of the proposed hierarchical energy management strategy are verified through experimental results.

Risk-based dynamic pricing by metaheuristic optimization approach for electric vehicle charging infrastructure powered by grid integrated microgrid system

This paper presents a risk-based dynamic pricing strategy for the microgrid based electric vehicle charging station. The microgrid is composed of renewable resources such as solar energy, wind energy and fuel cells. The proposed microgrid-based charging station operates off-grid and on-grid depending on the charging demand and trades the electricity with the main grid when needed. In this work, to increase the reliability and to decrease the detrimental impact of electric vehicle load on the distribution network electric vehicles act as a source of flexibility through grid to vehicle and vehicle to grid provisions. The uncertainty of solar and wind sources is a key issue that is efficiently addressed in this work along with one of the electric vehicles load uncertain factors such as state of charge variation using a probabilistic method. Two types of competent time-based dynamic pricing strategies are employed in this work to enhance the collective profit and to curtail the peak-to-valley ratio of charging station and electricity purchase cost of the electric vehicle owner. To deal with the uncertainty in the electricity market and its associated risk, the average value-at-risk is applied. The proposed multi-objective problem is tested on an IEEE 33 bus-modified system using MATLAB software. A metaheuristic fire hawk optimization technique is employed to implement dynamic pricing on charging station for two cases. To observe the efficiency of the applied algorithm, the findings of this work are compared with the other techniques described in the literature.

Research on Probability Estimation Method of Investment Cost of Water Conservancy Construction Project

The construction period of water conservancy construction project is long, and the project cost is affected by many uncertain factors. There is a great difference between the final account value after the completion of the project and the budget value before the commencement.Therefore, this paper considers the complexity and variability of the project cost, analyzes and constructs the probability estimation theory of investment cost from the perspective of system analysis, and applies it to the estimation of the investment cost of the construction project of Matan River miaotazi hydropower station, and calculates the probability distribution of the total cost change.The research results can provide a new idea for the estimation of investment cost of water conservancy projects, and also provide valuable reference for the estimation of investment cost of Matan River miaotazi hydropower station.

Ultra-short-term solar forecasting with reduced pre-acquired data considering optimal heuristic configurations of deep neural networks

<abstract> <p>Forecasting solar irradiance, particularly Global Horizontal Irradiance (GHI), has drawn much interest recently due to the rising demand for renewable energy sources. Many works have been proposed in the literature to forecast GHI by incorporating weather or environmental variables. Nevertheless, the expensive cost of the weather station hinders obtaining meteorological data, posing challenges in generating accurate forecasting models. Therefore, this work addresses this issue by developing a framework to reliably forecast the values of GHI even if meteorological data are unavailable or unreliable. It achieves this by leveraging lag observations of GHI values and applying feature extraction capabilities of the deep learning models. An ultra-short-term GHI forecast model is proposed using the Convolution Neural Network (CNN) algorithm, considering optimal heuristic configurations. In addition, to assess the efficacy of the proposed model, sensitivity analysis of different input variables of historical GHI observations is examined, and its performance is compared with other commonly used forecasting algorithm models over different forecasting horizons of 5, 15, and 30 minutes. A case study is carried out, and the model is trained and tested utilizing real GHI data from solar data located in Riyadh, Saudi Arabia. Results reveal the importance of employing historical GHI data in providing precise forecasting outcomes. The developed CNN-based model outperformed in ultra-short-term forecasting, showcasing average root mean square error results across different forecasting horizons: 2.262 W/m<sup>2</sup> (5min), 30.569 W/m<sup>2</sup> (15min), and 54.244 W/m<sup>2</sup> (30min) across varied day types. Finally, the findings of this research can permit GHI to be integrated into the power grid and encourage the development of sustainable energy systems.</p> </abstract>

QoS-driven resource allocation in fog radio access network: A VR service perspective.

While immersive media services represented by virtual reality (VR) are booming, They are facing fundamental challenges, i.e., soaring multimedia applications, large operation costs and scarce spectrum resources. It is difficult to simultaneously address these service challenges in a conventional radio access network (RAN) system. These problems motivated us to explore a quality-of-service (QoS)-driven resource allocation framework from VR service perspective based on the fog radio access network (F-RAN) architecture. We elaborated details of deployment on the caching allocation, dynamic base station (BS) clustering, statistical beamforming and cost strategy under the QoS constraints in the F-RAN architecture. The key solutions aimed to break through the bottleneck of the network design and to deep integrate the network-computing resources from different perspectives of cloud, network, edge, terminal and use of collaboration and integration. Accordingly, we provided a tailored algorithm to solve the corresponding formulation problem. This is the first design of VR services based on caching and statistical beamforming under the F-RAN. A case study provided to demonstrate the advantage of our proposed framework compared with existing schemes. Finally, we concluded the article and discussed possible open research problems.

An Overview of Software-Defined Radio Technology in CubeSat Communications

Small satellites are becoming more popular because they are cost-effective, easy to assemble, and use commercially available parts. However, traditional ground stations that communicate with small satellites require a lot of hardware and are expensive to build. This makes it challenging to get the most out of small satellites' information. Software Defined Radios (SDRs) have been developed to reduce the cost of these ground stations by doing many of the tasks in software. Many universities and organizations are developing SDR ground stations to communicate with satellites in different orbits. Communication with satellites is critical to their development and operation. This paper proposes a ground station that uses SDR technology to communicate with one or more small satellites in Low Earth Orbit (LEO). The station is cost-effective, portable, and easily scaled, allowing it to acquire data from satellites.

Reform of the European electricity market: Should we prefer a price based on a weighted average of marginal costs with cross-subsidies?

On the wholesale electricity market, the equilibrium price is set each hour on the basis of the marginal cost of the last power plant called, which is a gas-fired power plant a large part of the time in Europe. The surge in gas prices since the end of 2021 therefore largely explains the rise in the price of electricity. This paper analyses the reform projects proposed within the European Union to curb this surge in wholesale prices. It proposes then to reform the system by opting for pricing based on the weighted average hourly marginal costs, with financial compensation for power plants whose marginal cost is higher than this average. The quantitative study is conducted over the period January 1, 2020 through December 31, 2022 using ENTSOE hourly data. By implementing a compensation framework founded on average marginal costs, the market price experiences a significant decline, resulting in typically negative residual profits. Consequently, the fixed costs associated with the power plant fleet necessitate funding through the capacity market. This approach would enable French electricity consumers to align their payments with the structure of the national electricity fleet, specifically reflecting the average costs of power stations characterized by substantial fixed costs, such as nuclear facilities. The adoption of this proposed system is poised to expedite the energy transition toward a low-carbon economy.

Reducing the Ecological Footprint and charging cost of electric vehicle charging station using renewable energy based power system

Ecological Footprint of electric vehicle (EV) charging stations primarily focuses on three parameters: direct/indirect emissions, manpower and physical land requirement. Electric vehicle charging stations rely on electricity to charge EV batteries, and electricity source can significantly influence their environmental impact. The Ecological Footprints of EV charging station is estimated as 40.69 gha. The potential EF reduction of EV charging station is 89.9 % by using proposed hybrid power system. The Ecological Footprint of EV charging is about 3.1 × 10−4 gha/kWh of batteries charging. However, proposed hybrid system may reduce environmental impact of battery charging as 3.15×10−5 gha/kWh of batteries charging. The lifecycle cost of batteries charging is estimated as 0.168 $/kWh. It may reduce as 0.107 $/kWh of batteries charging with installation of proposed hybrid system. Thus, it is crucial to promote the use of renewable energy sources to power electric vehicle charging stations and minimize their environmental impact.