Energy Decomposition of Long and Middle Term Contract in Multi-Energy System

Huang, Yongxi; Wang, Xiuli; Zhang, Wei; Cao, Chunlian

doi:10.1109/isgt-asia.2019.8880942

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…Combining Equations ( 1)-( 6), we can obtain the total daily electricity costs E day_sum , as shown in Equation (7), and can obtain Equation (8) through consolidation.…”

Section: China's Electric Power Spot Market Trading Rulesmentioning

confidence: 99%

“…At present, there are few studies on the decomposition of the daily load curve on the electricity consumption side. Referring to the research on the power generation side's power decomposition in the literature [7,9,10,40], the aim of the decomposition of the daily load curve is to reduce the difference in the load rate of the power generation side units with the constraint of the randomness of new energy and the natural conditions of cascade hydropower. Since this paper seeks to reduce the electricity cost of electricity retailers, the optimization objective function provides the minimization of electricity cost in the DA market, as shown in Equation ( 21), which complete the decomposition of the daily load curve Q y_day,t .…”

Section: Optimization Objective Of Load Curve Decompositionmentioning

confidence: 99%

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Medium- and Long-Term Trading Strategies for Large Electricity Retailers in China’s Electricity Market

et al. 2022

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In the rapid promotion of China’s electricity spot market, a large number of electricity retailers and large consumers participate in power trading, of which medium- and long-term power trading accounts for a large proportion. In the electricity spot market, the previous medium- and long-term transactions need to be closely combined with the current spot market transaction settlement rules. This paper analyzes the trading strategy of large retailers in the power market. In order to effectively reduce the total electricity cost, it is necessary to optimize the medium- and long-term transactions based on three aspects: electricity quantity and benchmark price decisions of medium- and long-term contracts, the daily electricity decomposition method in the day-ahead (DA) market, and the daily load curve decomposition strategy. According to load history characteristics that are extracted by the X12 method, daily electricity is decomposed from the medium- and long-term electricity quantity in the DA market. This paper introduces three methods of decomposing the daily load curve and proves that the particle swarm algorithm is the best method for effectively minimizing the cost in the DA market. Through analyzing the total electricity cost change pattern, we prove that the basic component of decision making is the relative relationship between the electricity price of medium- and long-term contracts and the equivalent kWh price of medium- and long-term electricity in the DA market, which is determined by the decomposition daily curve method. If the equivalent kilowatt-hour price obtained by the decomposition method in the DA market is greater than the electricity price of medium- and long-term contracts, the larger the electrical energy of medium- and long-term contracts, the lower the costs. Based on the above principles, electricity retailers can carry out planning for medium- and long-term transactions, as well as the decomposition and declaration of the daily electricity quantities and daily load curves.

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“…Combining Equations ( 1)-( 6), we can obtain the total daily electricity costs E day_sum , as shown in Equation (7), and can obtain Equation (8) through consolidation.…”

Section: China's Electric Power Spot Market Trading Rulesmentioning

confidence: 99%

Section: Optimization Objective Of Load Curve Decompositionmentioning

confidence: 99%

Medium- and Long-Term Trading Strategies for Large Electricity Retailers in China’s Electricity Market

et al. 2022

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“…On a timeline, the balancing market (BM) is the last stage for creating offers from generating units to increase/ decrease the output so as to balance the system and adjust the difference between notifications and real-time operation. On long and mid-term, both producers and suppliers sign contracts to sell/buy electricity at lower prices [26,27]. the long-term contracts regulated market (LTCRM) is characterized by standard products that are rigid especially for RES generation that can be better predicted closer to the real-time operation [28,29].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms for Power System Sign Classification and a Multivariate Stacked LSTM Model for Predicting the Electricity Imbalance Volume

Bâra,

Oprea

2024

Int J Comput Intell Syst

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The energy transition to a cleaner environment has been a concern for many researchers and policy makers, as well as communities and non-governmental organizations. The effects of climate change are evident, temperatures everywhere in the world are getting higher and violent weather phenomena are more frequent, requiring clear and firm pro-environmental measures. Thus, we will discuss the energy transition and the support provided by artificial intelligence (AI) applications to achieve a cleaner and healthier environment. The focus will be on applications driving the energy transition, the significant role of AI, and collective efforts to improve societal interactions and living standards. The price of electricity is included in almost all goods and services and should be affordable for the sustainable development of economies. Therefore, it is important to model, anticipate and understand the trend of electricity markets. The electricity price includes an imbalance component which is the difference between notifications and real-time operation. Ideally it is zero, but in real operation such differences are normal due to load variation, lack of renewable energy sources (RES) accurate prediction, unplanted outages, etc. Therefore, additional energy has to be produced or some generating units are required to reduce generation to balance the power system. Usually, this activity is performed on the balancing market (BM) by the transmission system operator (TSO) that gathers offers from generators to gradually reduce or increase the output. Therefore, the prediction of the imbalance volume along with the prices for deficit and surplus is of paramount importance for producers’ decision makers to create offers on the BM. The main goal is to predict the imbalance volume and minimize the costs that such imbalance may cause. In this chapter, we propose a method to predict the imbalance volume based on the classification of the imbalance sign that is inserted into the dataset for predicting the imbalance volume. The imbalance sign is predicted using several classifiers and the output of the classification is added to the input dataset. The rest of the exogenous variables are shifted to the values from previous day d − 1. Therefore, the input variables are either predicted (like the imbalance sign) or are known from d − 1. Several metrics, such as mean average percentage error (MAPE), determination coefficient R2 and mean average error (MAE) are calculated to assess the proposed method of combining classification machine learning (ML) algorithms and recurrent neural networks (RNN) that memorize variations, namely long short-term memory (LSTM) model.

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“…In terms of medium-and long-term market curve decomposition methods, current studies mainly focus on the decomposition of power [12][13][14]. The decomposition of power refers to a method used in the context of energy markets, specifically for breaking down medium-and long-term power contracts into smaller, more manageable timeframes, typically for operational or trading purposes.…”

Section: Introductionmentioning

confidence: 99%

Optimization Decomposition of Monthly Contracts for Integrated Energy Service Provider Considering Spot Market Bidding Equilibria

Wu,

Wei,

Jiang

et al. 2024

Electronics

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Under the current power trading model, especially in the context of the large-scale penetration of renewable energy and the rapid integration of renewable energy into the power system, reasonable medium- and long-term decomposition can reduce the fluctuation in the energy price when the integrated energy service provider (IESP) participates in the spot market. It helps to avoid the price risk of the spot market. Additionally, it promotes the optimization of the operation of the regional energy day-ahead scheduling. At the present stage, most of the medium- and long-term contract decomposition methods focus on the decomposition of a single power and take less consideration of the bidding space in the spot market. This limitation makes it challenging to achieve efficient interaction and interconnection among multi-energy resources and smooth integration between the medium- and long-term market and the spot market. To address these issues, this paper proposes an optimal monthly contract decomposition method for IESPs that takes into account the equilibrium of spot bidding. First, the linking process and rolling framework of multi-energy transactions between the medium- and long-term market and the spot market are designed. Second, an optimal decomposition model for monthly contracts is constructed, and a daily decomposition method for monthly medium- and long-term contracts that accounts for the spot bidding equilibrium is proposed. Then, the daily preliminary decomposition result of medium- and long-term multi-energy contracts is used as the boundary condition of the day-ahead scheduling model, and the coupling characteristics of the multi-energy networks of electricity, gas, and heat are taken into account, as well as the operational characteristics. Then, considering the coupling characteristics and operating characteristics of electricity, gas, and heat networks, the optimal scheduling model of a multi-energy network is constructed to minimize the sum of cumulative daily operating costs, and the monthly final contract decomposition value and daily spot bidding space are derived. Finally, examples are calculated to verify the validity of the decomposition model, and the examples show that the proposed method can reduce the variance in spot energy purchase by about 4.64%, and, at the same time, reduce the cost of contract decomposition by about USD 0.33 million.

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Energy Decomposition of Long and Middle Term Contract in Multi-Energy System

Cited by 7 publications

References 5 publications

Medium- and Long-Term Trading Strategies for Large Electricity Retailers in China’s Electricity Market

Medium- and Long-Term Trading Strategies for Large Electricity Retailers in China’s Electricity Market

Machine Learning Algorithms for Power System Sign Classification and a Multivariate Stacked LSTM Model for Predicting the Electricity Imbalance Volume

Optimization Decomposition of Monthly Contracts for Integrated Energy Service Provider Considering Spot Market Bidding Equilibria

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