Electricity Purchase Cost Research Articles

Strategy of Large-Scale Electric Vehicles Absorbing Renewable Energy Abandoned Electricity Based on Master-Slave Game

The rapid development of renewable energy power has improved global energy and environmental problems. However, with the high volatility of renewable energy, it is an important challenge to guarantee the consumption of renewable energy and the reliable operation of high percentage renewable energy power systems. To solve this problem, this paper proposes a tracking absorption strategy for renewable energy based on the interaction between the supply side and the demand side, which adjusts the charging process of electric vehicles (EVs) through electric vehicle aggregator (EVA) to realize the tracking absorption of renewable energy abandoned electricity. In view of this process, we analyze the interaction among power grid, EVA and renewable energy generation (REG) as well as their market characteristics. The master-slave game model of EVA and REG was constructed considering the charging behavior characteristics of EVs and the output characteristics of REGs. Then the model solving strategy based on soft actor-critic (SAC) algorithm is proposed, and the REG pricing strategy and EVA scheduling strategy are calculated to optimize the mutual benefits. The case analysis shows that, under the same scale of electric vehicles, the proposed method can promote about 93.89% of the power abandonment consumption of wind power system, 96.00% of the photovoltaic system, and 97.41% of the wind-solar system. This strategy reduces the electricity purchase cost of EVA, promotes the interaction among renewable energy, vehicles and power grid, and improves the utilization efficiency of renewable energy.

Coordination of tradable carbon emission permits market and renewable electricity certificates market in China

Abstract In order to assist the low-carbon transformation of power sector, China is planning to establish the national Tradable carbon emission permits (TEPs) market and Renewable Electricity Certificates (RECs) market under mandatory carbon caps and Renewable Portfolio Standards (RPS) targets simultaneously. How to coordinate the two markets and what the coordination effects are become key issues. In the present study, a multi-region multi-market equilibrium model considering both mechanisms was developed and applied to China as the case study. The model established the effective coordination interval between RPS targets and carbon caps in which the two markets can operate jointly. Then the coordinated markets were proved to be more economically efficient so long as the government pursues the dual goal of reducing carbon emission and promoting renewable energy in sync. Finally, the theoretical and practical advantages of market coordination compared with uncoupled operation of these markets, such as diversifying markets risk, keeping TEP/REC price at lower level, and reducing consumers' electricity purchase cost, were discussed.

Optimal planning and investment benefit analysis of shared energy storage for electricity retailers

With the rapid development of energy storage (ES) technology, it has gradually become a vital facility to cope with the intermittent renewable generation and reduce the users’ electricity purchase cost. However, the limited application of the ES has suffered from its high capital cost. This paper proposes an approach of optimal planning the shared energy storage based on cost-benefit analysis to minimize the electricity procurement cost of electricity retailers. First, the multi-time scale electricity purchase model is established. Then the retailers are screened and classified based on the proposed matching degree function to select the collective of retailers, which maximizes the profits of planning the shared ES. The life cycle cost model and the equivalent cycle life method are used to evaluate the benefit of investing the shared ES. The benefit distribution among the collective is conducted based on the contribution degree of each retailer. In the case study, the optimization results of the shared ES for high-matching and low-matching groups are compared in detail. The simulation results illustrate that the costs are reduced by 8.83% and 8.03% respectively for the two groups of electricity retailers by the proposed approach. Results also verify that ES can effectively reduce the cost of retailers, and high matching degree can be used as the selection criterion to obtain a greater benefit of the shared ES.

A Time-phased Order Charging Strategy for an Electric Vehicle Photovoltaic Charging Station

Introduction Aiming at the problem of orderly charging of electric vehicle photovoltaic charging stations， the paper aims to establish a bucketed order charging strategy. Method The structure and working principle of the electric vehicle photovoltaic charging station were studied， and the charging conditions of the electric vehicle and the state of charge of the energy storage battery were constructed. During the peak load period， while meeting the vehicle charging demand， reduces the power grid purchases and the cost of electricity purchase， assisted the power grid to “eliminate the peak”. During the low load period， while meeting the vehicle charging demand， increased grid purchases and assists the power grid to “fill the valley”. Result The validity of the model is verified by simulation examples， compared with instant charging solutions， shows the advantages of the builted model in reducing the cost of electricity purchase and reducing the peak-to-valley difference of the power grid. Conclusion The charging strategy of the proposed electric vehicle photovoltaic charging station is correct and effective， and can provide reference for practical applications.

Study on the Joint Optimization Mode of Electric Energy and Regulation Market

Introduction As the guarantee for the stable of the power system， the marketization of the regulation auxiliary service has been put on the agenda. The energy-regulation joint optimization becomes a mode of market organization. This paper aims to study the combined optimization model of the electric energy-regulation market considering the performance difference of frequency modulation resources in the PJM market background. Method Regulation auxiliary service pricing adjustment model and the joint clearing model of electric energy and regulation market considering the difference of fast and slow regulation resources and regulation performance was proposed in this paper， which were applied to the simulation of a real power grid to compared with domestic electric energy - regulation step clearing market. Two typical scenarios were set up in wet season and dry season. The clearing of regulation resources， the influence on the electricity purchase cost in the spot market and the active degree of regulation market were studied in detail. Result In the stepwise clearing mode， the rapid regulation resource has obvious advantages and causes the increase of electricity purchase cost in the electricity market. In the electricity-regulation joint optimization model， the clearing results of fast and slow regulation resources are affected by the pricing adjustment strategy and the increase rate of electricity purchase costs in the power market is smaller than that in the stepwise model， and the clearing price and fluctuation degree in the regulation market are higher than that in the stepwise clearing mode model. Conclusion The combined optimization mode of electric-regulation modulation with reasonable price adjustment strategy is helpful to realize reasonable competition among units with different regulation performance and stimulate market vitality.

Operation of Solar-Storage-Hydrogen-Charging System by Value Stream Analysis

The topology of the clean energy system is a flexible structure with PV, wind power, hydrogen storage, battery storage. Thus, the construction and operation of the clean energy system should pay more attention to the temporal and spatial variations. This paper discussed a solar-storage-hydrogen-charging demonstration to accommodate the diverse temporal and spatial features of the clean energy system. The present study developed a Modelica library to support the cooperative control of this clean energy system based on value stream analysis. The result shows that the sold electricity profit of the electric storage system can reach 2.3 times than the electricity purchase cost. Though the hydrogen charging demand in the demonstration is small compared with the electric load demand, the hydrogen charging profit can be 1.7 times of the energy cost.

A day-ahead power spot market clearing model considering incentive-based demand response

It is a gradual evolution path and the most common practice in the initial stage of power spot market construction in China to start with bidding without price on the demand side, which lead to a question of how to solve the demand respond (DR) in day-ahead power spot market. To this end, a day-ahead power market clearing model considering incentive-based demand response (IBDR) is proposed. The IBDR is mainly aimed at large users with certain load elasticity, which can adjust their production schedules and change their electricity load curve to obtain certain cost incentives. This clearing method can optimize the spot market clearing result to maximize the social benefit by coordinating the electricity purchase cost and DR incentive cost. In this process, large users can obtain certain economic compensation by providing DR and help the dispatching mechanism to solve the operation problems such as power grid congestion, which could improve the operation efficiency of thermal power units. Finally, a case study based on the IEEE-30 buses system is used to verify the effectiveness of the proposed method.

Real-Time Stochastic Optimization of Energy Storage Management Using Deep Learning-Based Forecasts for Residential PV Applications

A computationally proficient real-time energy management method with stochastic optimization is presented for a residential photovoltaic (PV)-storage hybrid system comprised of a solar PV generation and a battery energy storage (BES). Existing offline energy management approaches for day-ahead scheduling of BES suffer from energy loss in real time due to the stochastic nature of load and solar generation. On the other hand, typical online algorithms do not offer optimal solutions for minimizing electricity purchase costs to the owners. To overcome these limitations, we propose an integrated energy management framework consisting of an offline optimization model concurrent with a real-time rule-based controller. The optimization is performed in receding horizon with load and solar generation forecast profiles using deep learning-based long short term memory method in rolling horizon to reduce the daily electricity purchase costs. The optimization model is formulated as a multistage stochastic program where we use the stochastic dual dynamic programming algorithm in the receding horizon to update the optimal set point for BES dispatch at a fixed interval. To prevent loss of energy during optimal solution update intervals, we introduce a rule-based controller underneath the optimization layer in finer time resolution at the power electronics converter control level. The proposed framework is evaluated using a real-time controller-hardware-in-the-loop test platform in an OPAL-RT simulator. The proposed real-time method is effective in reducing the net electricity purchase cost compared to other existing energy management methods.

A distributed EV charging strategy in an integrated energy system

Electric vehicles (EV) are viewed as an environmental-friendly travel device but bring the driver with range anxiety. One of the solutions to tackle this issue is to recharge at the fast charging station (FCS). Since the traffic flow of the transportation network and the operation condition of the fast charging station varies from time to time, it is important to implement real-time charging guiding for the EV drivers. In this paper, we propose a distributed guiding method to search for the best FCS with minimum sum of time cost and electricity purchasing cost. The EVs are viewed as a distribution of spatial and temporal electrical loads, which would affect the locational marginal price (LMP) of the FCS. The varied LMPs would also react upon the EV driver’s choice. The proposed guiding method utilizes the wireless communication technologies. Simulation test demonstrates the effectiveness of the proposed EV guiding method in an integrated energy system.

Optimal Dispatch of Integrated Energy Campus Microgrids Considering the Time-Delay of Pipelines

Energy transmission in cooling/heating and gas pipelines has time-delay, which impacts the power balance between various sources and loads in the optimal dispatch of an integrated energy campus microgrid (IECM). This article proposed an optimal dispatch model for IECMs, which considered the time-delay of energy transmission in cooling/heating and gas pipelines. In this model, the electricity and natural gas purchase cost of the IECM was made into an objective function. Partial differential equations (PDEs) were used to describe the time-delay in pipelines, and the discrete variables that described the switching number of absorption chillers and heat exchange units were included in the constraints. The proposed model was essentially a mixed-integer nonlinear programming (MINLP) model with PDE constraints. Orthogonal collocation on finite elements (OCFE) in the two-dimensional domain was used to transform PDEs into algebraic equations (AEs). Several linearization methods, including piecewise linearization and the big M method, were used to transform the initial MINLP model into a mixed-integer linear programming (MILP) model to reduce computational complexity. OCFE was compared with the first-order finite difference method, and simulation results were used to demonstrate the accuracy and efficiency of the proposed algorithm. The impact of the time-delay of pipelines on IECM was analyzed through comparison with a steady-state model.

Coordinated Control of PEV and PV-Based Storages in Residential Systems Under Generation and Load Uncertainties

Energy storage deployment in residential and commercial applications is an attractive proposition for ensuring proper utilization of solar photovoltaic (PV) power generation. Energy storage can be controlled and coordinated with PV generation to satisfy electricity demand and minimize electricity purchases from the grid. For optimal energy management, PV generation and load demand uncertainties need to be considered when designing a control method for the PV-based storage system. Another resource available at the residential level is the plug-in electric vehicle (PEV) which also has bi-directional power flow capability. The charging and discharging routines of the PEV can be controlled to help reduce the energy drawn from the power grid during peak hours. In this paper, a method of coordinated optimal control between PV-based storage and PEV storage is proposed considering the stochastic nature of solar PV generation and load demand. The stochastic dual dynamic programming algorithm is employed to optimize the charge/discharge profiles of PV-based storage and PEV storage to minimize the daily household electricity purchase cost from the grid. Simulation analysis shows the advantage of the coordinated control compared to other control strategies.

Consortium Blockchain-Based Microgrid Market Transaction Research

The microgrid trading market can effectively solve the problem of in-situ consumption of distributed energy and reduce the impact of distributed generation (DG) on the grid. However, the traditional microgrid trading model has some shortcomings, such as high operation cost and poor security. Therefore, in this paper, a microgrid market trading model was developed using consortium blockchain technology and Nash game equilibrium theory. Firstly, blockchain container is used to authenticate the users who want to participate in the transaction. Then, the pricing system collects and integrates transaction requests submitted by users, then formulates transaction pricing strategy of microgrid using Nash equilibrium theory. Finally, the price, trading volume, and user information are submitted to the blockchain container for transaction matching to achieve the transaction. After the transaction is completed, its related information is recorded in the hyperledger and the dispatch system is called. The scene simulation was implemented on Fabric 1.1 platform and the results analyzed. Results show that the trading model proposed in this paper greatly reduces the cost of electricity purchase and improves the benefits of electricity sales. Besides, the model is far more capable of handling transactions than the models based on Bitcoin and Ethereum.

Evaluating Levelized Cost of Storage (LCOS) Based on Price Arbitrage Operations: with Liquid Air Energy Storage (LAES) as an Example

Liquid air energy storage (LAES) is a novel proven technology that can increase flexibility of the power network, obtaining revenue through energy price arbitrage. To assess the economic potential of a variety of energy storage options, this study develops a cost research framework for LAES, which is also applicable to other energy storage technologies. For the calculation of Levelized Cost of Storage (LCOS), it is essential to evaluate the electricity purchasing cost and the total electricity generated. However, they are often estimated by simply assuming an average electricity price and an annual operating cycle in the previous studies. In this paper, a price arbitrage algorithm is developed, according to which decisions are made at each time step whether to charge, discharge or stand by. Thus, the electricity purchasing cost as well as the amount of electricity generated by the storage unit is determined and the LCOS of the energy storage system is calculated. Results show that the LCOS for a 25MW/125MWh LAES system is in the range 191-590 £/MWh, depending on different round-trip efficiencies and different costs set in three scenarios. If the round-trip efficiency is assumed to be 60%, the LCOS would in the range 191-294 £/MWh under the three scenarios.

Research on investment input-output benefit of distribution network planning considering micro-grid and multi-load development under the new power reform

This paper studies the input-output model considering microgrid and multi-load microgrid. Combined with the new power reform, the investment strategy of the distribution network is analyzed and evaluated. The input model considers depreciation of fixed assets, operation and maintenance fees, and electricity purchase costs. The output model considers the benefits of distribution services and the benefits of electricity sales. On this basis, the uncertain factors in grid investment are studied, and the probability distribution function is constructed. The “incremental method” is used to analyze the influence of uncertain factors on the evaluation index of the reform scheme, and the most sensitive factor is obtained. Finally, the case study shows that the input-output model can better reflect the evaluation indicators of the transformation plan and provide decision-making basis for the grid investment transformation plan.

Online optimization for residential PV-ESS energy system scheduling

This paper studies a residential PV-ESS energy system scheduling problem with electricity purchase cost, storage degradation cost and surplus PV generated cost [ 2 ]. This problem can be viewed as an online optimization problem in time \begin{document}$ t \in [1, T] $\end{document} with switching costs between decision at \begin{document}$ t-1 $\end{document} and \begin{document}$ t $\end{document} . We reformulate the problem into a single variable problem with \begin{document}$ {\bf{s}} = (s_1, ..., s_T)^T $\end{document} , which denotes the storage energy content. We then propose a new algorithm, named Average Receding Horizon Control (ARHC) to solve the PV-ESS energy system scheduling problem. ARHC is an online control algorithm exploiting the prediction information with \begin{document}$ W $\end{document} -steps look-ahead. We proved an upper bound on the dynamic regret for ARHC of order \begin{document}$ O(nT/W) $\end{document} , where \begin{document}$ n $\end{document} is the dimension of decision space. This bound can be converted to a competitive ratio of order \begin{document}$ 1+O(1/W) $\end{document} . This result overcomes the drawback of the classical algorithm Receding Horizon Control (RHC), which has been proved [ 11 ] that it may perform bad even with large look ahead \begin{document}$ W $\end{document} . We also provide a lower bound for ARHC of order \begin{document}$ O(nT/W^2) $\end{document} on the dynamic regret. ARHC is then used to study a real world case in residential PV-ESS energy system scheduling.

Optimizing Photovoltaic Operation Control for Community with CCHP under Economy Scales

With the rapid development of industry, carbon emissions and other environmental problems have become increasingly prominent due to the consumption of fossil fuels, which can be alleviated by changing the mode of energy utilization and utilizing renewable energy. Because of its high energy efficiency, less pollution and the ability to realize the cascade utilization of energy, the combined cooling heating and power (CCHP) system will become the development trend of the distributed energy supply system in the future. In this paper, solar energy refrigeration mode, photovoltaic power supply mode, and waste heat refrigeration technology are used to construct the control method of the power supply mode of CCHP system with solar energy in the park and establish two kinds of energy supply modes including fuel cost and electricity purchase cost respectively. Considering the deviation of solar power output prediction, the stochastic objective functions are described by α-super-quantile method and the optimization model is established. The simulation results are solar energy parameters. The economic benefit of CCHP system running in cooling mode is higher than that of power supply mode, which can provide new ideas for the efficient and rational utilization of renewable energy under the background of smart grid.

Energy management and optimal storage sizing for a shared community: A multi-stage stochastic programming approach

The aim of this paper is to propose a new energy management framework and storage sizing for a community composed of multiple houses and distributed solar generation. Uncertainties associated with solar generation and electricity demand are included to make the mathematical models more realistic, and as a result, provide more accurate control strategies to manage storage devices utilization. To evaluate that, a multi-stage stochastic program model designed to minimize community electricity purchase cost per day is used to support decision-making by creating control policies for energy management. Two different strategies are created to represent the interest of a single household (the individual energy management - IEM) and households that share their assets with the community (shared energy management - SEM). Our strategies consider time-of-use rates (ToU), load and resource variation during different seasons, with their distinct days of the year, to calculate net present value (NPV) associated with the energy savings. IEM and SEM are then used in a framework designed to establish the requirement of optimal energy storage size for each house of the community based on NPV values. The results of this study for an analysis considering a community with five houses show that the proposed SEM strategy reduces the overall electricity purchase costs for a summer day up to 11% and 3% compared with heuristic and IEM control respectively. Moreover, our results suggest that the application of the methodology increases peak energy savings up to 17%, scales up solar generation usage up to 23%, and the optimal storage size obtained in the shared community case reduces up to 50%.

HOUSEHOLDS ELECTRICITY USAGE ANALYSIS AND THE EFFECTIVENESS OF CHANGING TARIFF GROUP

After the introduction of the Energy Law in 1997, many enterprises started to operate in the power industry, which is translated into the electricity prices competitiveness. The further household tariff extension with more expensive rates at peak times, and favorable lower rates during the night and off-peak hours, did not result significantly change among users belonging to the most generic household tariff G11 with a single electricity rate per KWh (Kilowatt hour) regardless time of use. The article analyzes the electricity purchase and supply costs of the largest households group belonging to G11 tariff and simulates the effectiveness of moving from existing tariff group to the offered competitive tariff according to electricity suppliers.

Coordinated charging and discharging strategies for plug‐in electric bus fast charging station with energy storage system

Plug-in electric bus (PEB) is an environmentally friendly mode of public transportation and PEB fast charging stations (PEBFCSs) play an essential role in the operation of PEBs. Under effective control, deploying an energy storage system (ESS) within a PEBFCS can reduce the peak charging loads and the electricity purchase costs. To deal with the (integrated) scheduling problem of (PEBs charging and) ESS charging and discharging, in this study, the authors propose an optimal real-time coordinated charging and discharging strategy for a PEBFCS with ESS to achieve maximum economic benefits. According to whether the PEB charging loads are controllable, the corresponding mathematical models are, respectively, established under two scenarios, i.e. coordinated PEB charging scenario and uncoordinated PEB charging scenario. The price and lifespan of ESS, the capacity charge of PEBFCS and the electricity price arbitrage are considered in the models. Further, under the coordinated PEB charging scenario, a heuristics-based method is developed to get the approximately optimal strategy with computation efficiency dramatically enhanced. Finally, the authors validate the effectiveness of the proposed strategies, interpret the effect of ESS prices on the usage of ESS and provide the sensitivity analysis of ESS capacity through the case studies.

Distributionally Robust Optimal Bidding of Controllable Load Aggregators in the Electricity Market

An optimal bidding model of controllable loads is proposed to minimize the worst-case conditional expectation of electricity purchase cost simultaneously in day-ahead and real-time markets. By reformulating the worst-case conditional value-at-risk (CVaR) constraints, a solvable semi-definite program (SDP) is presented to relax the moment uncertainty of electricity price and simultaneously determine the optimal day-ahead bid and real-time increment/decrement bid.