Electricity Market Transactions Research Articles

Optimal scheduling of electricity-hydrogen coupling virtual power plant considering hydrogen load response

With the rapid development of hydrogen production by water electrolysis, the coupling between the electricity-hydrogen system has become closer, providing an effective way to consume surplus new energy generation. As a form of centralized management of distributed energy resources, virtual power plants can aggregate the integrated energy production and consumption segments in a certain region and participate in electricity market transactions as a single entity to enhance overall revenue. Based on this, this paper proposes an optimal scheduling model of an electricity-hydrogen coupling virtual power plant (EHC-VPP) considering hydrogen load response, relying on hydrogen to ammonia as a flexibly adjustable load-side resource in the EHC-VPP to enable the VPP to participate in the day-ahead energy market to maximize benefits. In addition, this paper also considers the impact of the carbon emission penalty to practice the green development concept of energy saving and emission reduction. To validate the economy of the proposed optimization scheduling method in this paper, the optimization scheduling results under three different operation scenarios are compared and analyzed. The results show that considering the hydrogen load response and fully exploiting the flexibility resources of the EHC-VPP can further reduce the system operating cost and improve the overall operating efficiency.

Federated learning application in distributed energy trading in integrated energy system

Privacy protection in electricity market transactions is a long-term topic, and it must be considered in the application of any new power system project today. This paper explores a new welfare optimization method considering privacy protection in the field of energy trading based on federated learning (FL). First, this paper models an integrated energy system (IES) with one distributed network manager (DNM) and multiple load aggregators (LAs). This model adopts the idea of Stackelberg game, makes DNM dominate the trading game, and makes LAs as followers. Then, it uses the FL-Stackelberg method proposed for the first time to solve the established individual welfare optimization model participating in the game and compares the results with those of the traditional hierarchical optimization method and the Mathematical Program with Equilibrium Constraint optimization method. Numerical results at the end of the paper prove the good accuracy and calculation speed of the proposed FL-Stackelberg​ optimization algorithm, and demonstrate the reliability of FL in engineering practice of IES.

Transaction decision optimization of new electricity market based on virtual power plant participation and Stackelberg game.

This study intends to optimize the trading decision-making strategy of the new electricity market with virtual power plants and improve the transmission efficiency of electricity resources. The current problems in China's power market are analyzed from the perspective of virtual power plants, highlighting the necessity of reforming the power industry. The generation scheduling strategy is optimized in light of the market transaction decision based on the elemental power contract to enhance the effective transfer of power resources in virtual power plants. Ultimately, value distribution is balanced through virtual power plants to maximize the economic benefits. After 4 hours of simulation, the experimental data shows that 75 MWh of electricity is generated by the thermal power system, 100 MWh by the wind power system, and 200 MWh by the dispatchable load system. Comparatively, the new electricity market transaction model based on the virtual power plant has an actual generation capacity of 250MWh. In addition, the daily load power of the models of thermal power generation, wind power generation, and virtual power plant reported here are compared and analyzed. For a 4-hour simulation run, the thermal power generation system can provide 600 MW of load power, the wind power generation system can provide 730 MW of load power, and the virtual power plant-based power generation system can provide up to 1200 MW of load power. Therefore, the power generation performance of the model reported here is better than other power models. This study can potentially encourage a revised transaction model for the power industry market.

Research on the pricing strategy of park electric vehicle agent considering carbon trading

The large-scale use of electric vehicles provides the strong support for the realization of the decarbonization in the energy sectors. With the maturity of the carbon trading market, a large number of decentralized electric vehicles play an important role in the carbon market because of their zero emission characteristics. However, how the decentralized electric vehicles participate in the carbon market is a challenge in the present world. For the purpose of reducing the load peak-valley difference and guiding a large number of electric vehicles in the park to charge in an orderly manner, we put forward a pricing strategy for the park electric vehicle agent under the background of “carbon peaking and carbon neutralization”. A two-level Stackelberg game model is developed, in which the upper level maximizes the profits of park electric vehicle agent and the lower level minimizes the charging cost of the electric vehicle owners. The park electric vehicle agent participates in the carbon market by selling the carbon emission allowances owned by electric vehicles in the park to obtain subsidies and increase profits. The optimal pricing strategy of the electric vehicle agent can effectively guide the decentralized electric vehicle users in the park to participate in the electricity and carbon market transactions and reduce the peak-valley load difference. The commercial solver CPLEX based on YALMIP is used to solve the model, and a case study is given to verify the feasibility and validity of the proposed scheme.

Nash bargaining based collaborative energy management for regional integrated energy systems in uncertain electricity markets

The paradigm of the regional integrated energy system (RIES) with internal energy sharing, consisting of multiple geographically adjacent and interconnected multi-energy microgrids (MEMGs), is considered a strategic effort to empower low-carbon energy systems. However, various operational uncertainties and electricity market transaction rules pose challenges to fulfilling the functionalities and economic promises of RIES. This paper presents a collaborative energy management strategy for RIES based on the market rules, which is formulated as a three-phase structure (i.e., day-ahead bidding, real-time dispatch and revenue allocation) and integrates multiple time scales to support electricity settlement and fine-grained energy dispatch. The day-ahead bidding model is developed based on the hybrid stochastic and chance-constrained programming that can comprehensively address multiple uncertainties introduced by the electricity market, distributed renewable generation and multi-energy loads to decide the optimal bidding electricity. The actual operation decisions can be determined in the rolling model predictive control based real-time dispatch phase in coordination with the day-ahead bidding electricity. The revenue from energy sharing is fairly allocated to each MEMG based on Nash bargaining theory to stabilize the incentive for MEMGs to engage in collaboration. The effectiveness of the proposed solution is extensively assessed through numerical simulations in the Guangdong electricity market.

Operation Cost Optimization Method of Regional Integrated Energy System in Electricity Market Environment Considering Uncertainty

In the electricity market environment, the regional integrated energy system (RIES) can reduce the total operation cost by participating in electricity market transactions. However, the RIES will face the risk of load and electricity price uncertainties, which may make its operation cost higher than expected. This paper proposes a method to optimize the operation cost of the RIES in the electricity market environment considering uncertainty. Firstly, based on the operation cost structure of the RIES in the electricity market environment, the energy flow relationship of the RIES is analyzed, and the operation cost model of the RIES is built. Then, the electricity purchase costs of the RIES in the medium- and long-term electricity markets, the spot electricity market, and the retail electricity market are analyzed. Finally, considering the risk of load and electricity price uncertainties, the operation cost optimization model of the RIES is established based on conditional value-at-risk. Then it is solved to obtain the operation cost optimization strategy of the RIES. Verification results show that the proposed operation cost optimization method can reduce the operation cost of high electricity price scenario by optimizing the energy purchase and distribution strategy, constrain the risk of load and electricity price uncertainties, and help balance the risks and benefits.

Study on the Application of Demand-Side Resources to Participate in the Electricity Ancillary Services Market

As the scope of pilot power auxiliary services continues to expand, major provincial power companies are clarifying the main status of demand-side response resources and energy storage resources as market participants one after another. Electricity auxiliary services are developing towards diversification, and a fair, transparent, competitive and orderly market mechanism for sharing auxiliary services is taking shape. The active participation of energy storage devices, demand-side resourcesand third parties in electricity market services will promote the development of new industries such as new energy consumption, electricity market transactions and virtual power plants. This paper summaries the participation patterns and trading mechanisms of demand-side resources in domestic and international markets, analyses how domestic demand response resources can be used to participate in the ancillary services market, and makes corresponding suggestions for the development of the ancillary services market in the domestic power industry.

Research on adjustable load dividend transmission strategy under aggregate operation mode

In recent years, the scale of inter-provincial and inter-regional power transmission keeps growing, the proportion of new energy keeps rising, and the complexity of power grid structure increases the risk of its operation control. Under the aggregation operation mode, the adjustable load has great electricity elasticity. Guiding its electricity consumption behavior and making it participate in the power grid regulation operation can greatly improve the safety of operation, promote the absorption of new energy and realize the optimal allocation of power resources. This paper studies the form of adjustable load participation in grid regulation in different scenarios under the aggregate operation mode, analyzes the cost and benefit of adjustable load participation in electricity market transactions, and then combines the evaluation method based on the contribution of adjustable load users in different scenarios to propose a dividend transmission strategy of adjustable load in the aggregate operation mode.

A Multi-Agent Optimal Bidding Strategy in Multi-Operator VPPs Based on SGHSA

As an individual plant participating in the power market, the virtual power plant (VPP) is regarded as the ultimate configuration of the energy Internet, and effective dispatching is a challenge. This paper proposes a multi-agent optimal bidding strategy based on a self-adaptive global optimal harmony search algorithm (SGHSA) to solve the problem of multi-operator participation in virtual power station scheduling. The method takes multiple agents to simulate the bidding process in the VPPs and distributes the profits for the operators based on the market mechanism to optimize the distributed energy resources (DERs). Case studies are provided and show that the proposed method realizes the optimal distribution of power generation and demand level, which improves the comprehensive advantage of the VPP in electricity market transactions.

A low‐carbon economic dispatch model for electricity market with wind power based on improved ant‐lion optimisation algorithm

AbstractIntroducing carbon trading into electricity market can convert carbon dioxide into schedulable resources with economic value. However, the randomness of wind power generation puts forward higher requirements for electricity market transactions. Therefore, the carbon trading market is introduced into the wind power market, and a new form of low‐carbon economic dispatch model is developed. First, the economic dispatch goal of wind power is be considered. It is projected to save money and reduce the cost of power generation for the system. The model includes risk operating costs to account for the impact of wind power output variability on the system, as well as wind farm negative efficiency operating costs to account for the loss caused by wind abandonment. The model also employs carbon trading market metrics to achieve the goal of lowering system carbon emissions, and analyze the impact of different carbon trading prices on the system. A low‐carbon economic dispatch model for the wind power market is implemented based on the following two goals. Finally, the solution is optimised using the Ant‐lion optimisation method, which combines Levi's flight mechanism and golden sine. The proposed model and algorithm's rationality is proven through the use of cases.

From Multi- to Univariate: A Product Random Variable with an Application to Electricity Market Transactions: Pareto and Student’s t-Distribution Case

Multivariate modelling of economics data is crucial for risk and profit analyses in companies. However, for the final conclusions, a whole set of variables is usually transformed into a single variable describing a total profit/balance of company’s cash flows. One of the possible transformations is based on the product of market variables. Thus, in this paper, we study the distribution of products of Pareto or Student’s t random variables that are ubiquitous in various risk factors analysis. We review known formulas for the probability density functions and derive their explicit forms for the products of Pareto and Gaussian or log-normal random variables. We also study how the Pareto or Student’s t random variable influences the asymptotic tail behaviour of the distribution of their product with the Gaussian or log-normal random variables and discuss how the dependency between the marginal random variables of the same type influences the probabilistic properties of the final product. The theoretical results are then applied for an analysis of the distribution of transaction values, being a product of prices and volumes, from a continuous trade on the German intraday electricity market.

Multi-time scale trading profit model of pumped storage power plant for electricity market

Pumped storage power plant (PSPP) has the upper hand on economy and cleanness. It also has the functions of frequency regulation, phase regulation, and spare, which have been instrumental in maintaining the stability of power system operation. But now the mechanism for PSPP to become involved in electricity market transactions in China is imperfect. How to properly establish a multi-time scale trading profit model and reasonably allocate the capacity of PSPP has been instrumental in realizing the economic operation of the power system. So, this article analyzes the mechanism for PSPP to become involved in electricity market trading by providing combined electricity supply services and ancillary services, and establishes an optimization model with respect to economic optimization. At the same time, considering the volatility of electricity prices in the spot market, the risk of PSPP becoming involved in electricity market trading is measured by conditional Value at Risk (CVaR) to achieve economic optimization while minimizing the risk. The case studies demonstrate that the proposed profit model can enhance the revenue and decrease the risk of PPSP.

Research on Coupled Cooperative Operation of Medium- and Long-Term and Spot Electricity Transaction for Multi-Energy System: A Case Study in China

Due to the intermittent and anti-peak shaving characteristics of the new energy generator sets, the phenomenon of power abandonment hinders direct participation in the electricity market transactions. The hybrid electricity market can use spot market transactions to absorb renewable energy to a large extent. The multi-energy complementary operation coupling of the hybrid electricity market transactions can exploit the complementation and substitution between different energy sources, realize flexible energy production, consumption, storage, and transmission, and optimize the allocation of resources on a larger scale. In this paper, a mid-long-term spot transaction coordination scheduling (MTCS) model for a multi-energy system is constructed by considering the medium- and long-term electricity market uncertainty and the trial operation characteristics of the spot power market in China. A two-stage solution method is introduced to solve the complex multi-agent, multi-period, and multi-energy model. The results of testing this model on the Gansu region, one of the first eight spot pilot areas in China, are presented and discussed in detail. The results showed that this MTCS model could reduce the opening of thermal power units to a more considerable extent, prioritize the consumption of new energy power generation, and reduce the output uncertainty of new energy through the hybrid power market.

Evaluation of the performance and economy for a hybrid energy storage system using hydrogen and compressed carbon dioxide as the energy carrier

A green concept of hybrid energy storage system with hydrogen and compressed carbon dioxide as the energy carrier has been proposed in this paper. The integration of the two energy storage methods leads to a hybrid efficient storage way, which can have higher energy density and lower pressure tank volume compared to the compressed carbon dioxide, and has higher energy storage efficiency compared with the hydrogen one. The ratio of the two energy storage is determined by the hydrogen consumption way. In this paper, the hydrogen fueled gas turbine with carbon dioxide as the working fluid (Allam cycle) is used for large amounts of the hydrogen consumption, and the mass flow rate ratio of hydrogen and CO2 is 0.0048 at the design point. The combined system can be used for inter-regional power dispatching, peak and frequency modulation and so on. A basic system form is firstly introduced, and the round trip efficiency (RTE) and levelized cost of electricity (LCOE) are 42.41% and 0.2075 $/kWh at the design point, respectively. In addition, two improved types are proposed. The effect of key parameters, including the CO2 compressor outlet pressure, the CO2 compressor inlet pressure and the CO2 mass flow rate, on the system performance is performed. The multi-objective optimization is also conducted. The comparison reveals that the improved form-I performs with a lower LCOE and a weaker RTE. In contrast, the improved form-II owns a better RTE but a higher LCOE. It can be concluded that the improved form-I can improve the economic performance of the energy storage system through the optimization of the compressed carbon dioxide high pressure storage subsystem. And the improved form-II can enhance the thermodynamic performance by adding the transcritical carbon dioxide cycle branch. Potential solutions to improve the system economic performances are also discussed and the results indicate that technology development, subsidies and electricity market transaction premium would be beneficial for the large-scale promotion of the system in practical.

Two-Part Tariff of Pumped Storage Power Plants for Wind Power Accommodation

Pumped storage power plants face many challenges in competing in the electricity market, and high pumping costs lead to high prices for their power generation, which is one of the important factors that has limited their development. To address this problem, this paper studies the pumped storage two-part tariff mechanism considering wind power accommodation and uses the peak-valley price difference of wind power to realize the rationality and economy of a pumped storage charging and discharging strategy. It can improve the competitiveness of pumped storage power plants participating in electricity market transactions. Then, by considering the economic advantages of “pumped storage + clean energy”, a pumped storage and wind power joint optimal dispatching model was established based on the original pumped storage pricing method. This model takes the total system cost reduction after the introduction of pumped storage as the objective function to derive a reasonable pumped storage strategy. After which, the two-part tariff for pumped storage power plants was formulated based on the principle of reasonable revenue. Finally, a sensitivity analysis of various relevant parameters of the power plant was conducted through case studies to verify the effectiveness of the two-part tariff mechanism of pumped storage. It was found that the electricity tariff is lowest when the ratio of plant capacity to upper reservoir capacity is 1:6.37 (MW/million m3).

Design and application of distributed photovoltaic transaction consensus mechanism based on blockchain

In recent years, distributed photovoltaic power generation has developed rapidly, but it has also brought many problems, such as poor equipment reliability, complex operation control, and many management subjects. Under the situation of the development of the electricity market, it is necessary to solve the problem of ensuring the data security of participating in the electricity market. Blockchain technology has the characteristics of traceability, non-tampering of information, openness and transparency, etc., which can satisfy massive transactions and reduce the burden of traditional power transactions. Therefore, based on the typical characteristics of blockchain, this paper analyzes the common attributes of blockchain and electricity market transactions, and adopts the DPOS (delegated proof of stake) consensus mechanism to realize distributed photovoltaic transactions. On this basis, this paper utilizes the consensus method based on node credit value and applies it to the actual microgrid distributed photovoltaic power station. The experimental results show that the transaction consensus mechanism proposed in this paper has certain advantages in transaction efficiency and transaction cost, which can provide a practical basis for blockchain to realize distributed photovoltaic transactions.

Inter-Hour Forecast of Solar Radiation Based on Long Short-Term Memory with Attention Mechanism and Genetic Algorithm

The installed capacity of photovoltaic power generation occupies an increasing proportion in the power system, and its stability is greatly affected by the fluctuation of solar radiation. Accurate prediction of solar radiation is an important prerequisite for ensuring power grid security and electricity market transactions. The current mainstream solar radiation prediction method is the deep learning method, and the structure design and data selection of the deep learning method determine the prediction accuracy and speed of the network. In this paper, we propose a novel long short-term memory (LSTM) model based on the attention mechanism and genetic algorithm (AGA-LSTM). The attention mechanism is used to assign different weights to each feature, so that the model can focus more attention on the key features. Meanwhile, the structure and data selection parameters of the model are optimized through genetic algorithms, and the time series memory and processing capabilities of LSTM are used to predict the global horizontal irradiance and direct normal irradiance after 5, 10, and 15 min. The proposed AGA-LSTM model was trained and tested with two years of data from the public database Solar Radiation Research Laboratory site of the National Renewable Energy Laboratory. The experimental results show that under the three prediction scales, the prediction performance of the AGA-LSTM model is below 20%, which effectively improves the prediction accuracy compared with the continuous model and some public methods.

Generalized Energy Storage Allocation Strategies for Load Aggregator in Hierarchical Electricity Markets

The uncertainty of user-side resource response will affect the response quality and economic benefit of load aggregator (LA). Therefore, this paper regards the flexible user-side resources as a virtual energy storage (VES), and uses the traditional narrow sense energy storage (NSES) to alleviate the uncertainty of VES. In order to further enhance the competitive advantage of LA in electricity market transactions, the opertion mechanism of LA in day-ahead and real-time market is analyzed, respectively. Besides, truncated normal distribution is used to simulate the response accuracy of VES, and the response model of NSES is constructed at the same time. Then, the hierarchical market access index (HMAI) is introduced to quantify the risk of LA being eliminated in the market competition. Finally, combined with the priority response strategy of VES and HMAI, the capacity allocation model of NSES is established. As the capacity model is nonlinear, Monte Carlo simulation and adaptive particle swarm optimization algorithm are used to solve it. In order to verify the effectiveness of the model, the data from PJM market in the United States is used for testing. Simulation results show that the model established can provide the effective NSES capacity allocation strategy for LA to compensate the uncertainty of VES response, and the economic benefit of LA can be increased by 52.2% at its maximum. Through the reasonable NSES capacity allocation, LA is encouraged to improve its own resource level, thus forming a virtuous circle of market competition.

Short-Term Electricity Load Forecasting Based on Temporal Fusion Transformer Model

Electricity load forecasting plays an important role in the operation of power systems. Inaccurate forecast would reduce the safety of power supply and affect the economic and social activities as well as national defense and security. In addition, the forecast results also support decision-making on electricity generation and market transactions. Traditional methods such as AR, ARIMA, SARIMA have been widely used to forecast short term electricity load. Recently, load forecasting based on artificial and deep neural networks have shown significant accuracy improvement over traditional statistical models. In this research, a novel recurrent neural network named temporal fusion transformer (TFT) is used to forecast short-term electricity load of Hanoi city. The TFT is a newly developed model and it combines the advantages of several other RNN models such as LSTM and the self-attention mechanism. In addition to historical load data, we use temperature and humidity features, and time features such as calendar month, lunar month, days of the week, hours of the day and holidays. The forecast results of TFT are compared with traditional statistical models as well as well-known RNN models. The compared results show that the proposed method is better than other methods in both MAE and MAPE criteria.

Data interaction model for power transaction based on CIM

With the increase of power transaction business, traditional power transaction data interaction models cannot fully meet the needs of data interaction. The data model conforming to the power transaction data architecture plays an important role. Data interaction requirements of Chinese electricity market transaction business are analyzed, and the specific method of power transaction data description is given in the IEC-61970 protocol standard. Also a common information model (CIM) for electricity trans-action data interaction is built that conforms to the data interaction architecture. Finally the functional description of the model is built adopting the unified modeling language (UML). Compared with the existing electricity transaction data interaction model, the method is beneficial to reduce the degree of data redundancy, increase the speed of data interaction, and thus improve the transaction efficiency of the electricity market.