A Calculation Model for Wind Power Consumed by Electric Heating Load Based on Multi-state Probability Distribution

Numerous problems have emerged with the rapid development of wind power in Xinjiang. The predominant problem is the inhibition of the healthy development of the wind power industry by wind power curtailment in Xinjiang. In this study, wind power heating and high energy-consuming industries were coupled to increase wind power consumption in Xinjiang on the load side. The feasibility analysis of wind power heating in Dabancheng showed that the heating load characteristics coincide with the wind characteristics and the electric heating technology can fulfill the requirements for wind power heating. A business model innovation achieved a win–win situation. Furthermore, a wind power heating system was designed in Dabancheng, the boiler capacity and the heat storage capacity were calculated, and a heating system model with heat storage technology was established. Wind power heating with heat storage can improve the consumption of wind power and increase the local electric load and system adjustability. Furthermore, heating with curtailed wind power can save 2942 tons of standard coal as well as reduce 5172 tons of CO2, 2.9 tons of SO2, 22 tons of NOX, and 2 tons of soot emissions. Non-grid-connected distributed wind power was applied to the high energy-consuming coal chemical industry to reduce wind power curtailment, expand the consumption market, and reduce pollution. A multifunctional wind-photovoltaic complementary system with hydrogen energy storage coupled with traditional high energy-consuming coal chemical industry was established. The implementation plan using wind and solar energy to produce, store, and apply hydrogen energy was proven beneficial. Wind power consumption was improved on the load side. The wind power curtailment problem was reduced by increasing local load using the curtailed wind power to provide clean heat in Dabancheng and constructing the multifunctional wind–photovoltaic complementary system. This study will contribute to the healthy and sustainable development of the wind power industry in Xinjiang.

ABSTRACTThe rapid development of renewable energy has promoted the research of the integrated energy system. In particular, the joint optimal scheduling of renewable energy and traditional thermal power units is the key technology to solve the current renewable energy integration and the source network load balance. At present, the total installed capacity of cogeneration units is huge, which hinders the integration of renewable energy and the flexibility of the power grid regulation. Therefore, this paper proposes a robust optimal scheduling strategy for the plant‐level integrated energy system considering electricity‐heat balance. The wind power and solar energy are connected in parallel with the heating network of cogeneration units to decouple heat and power in a more economical and flexible way, while improving the integration of renewable energy. Based on the principle of Kang's robust optimization, robust optimization, a linear robust optimization probability density bias prediction optimization method for the plant‐level integrated energy system considering the electric‐heat balance is proposed in this paper. The dynamic nonlinear constraints in the output process of thermal and electrical loads are transformed into linear, which takes into account the conservatism of robust optimization and the economy of the objective function. The proposed optimization algorithm facilitates the integration of a high proportion of renewable energy into the power grid and is also applicable to other integrated energy systems with nonlinear constraint characteristics.

Among northern China, CHP is the main electric and heating source to provide electricity and heat. While, with the increasing integration of wind power, the peak regulation capacity is lacking. Wind power curtailment occurs frequently because of the contradiction between heating and electric load. District heating system plays the majority role in heating supply because of its efficiency and cleanness. There are enormous heat storage capacity contained in district heating system, if the stored heat energy could be utilized efficiently the contradiction between heating and electric load could be relieved and the wind power curtailment ld be avoided considerably. This study presents an approach that takes advantage of the heat in the primary heating circuit to absorb surplus wind power. The expressions of the components in heating system are given to establish the mathematical model. Followed, the thermodynamic properties of heating pipelines are analyzed, the heat storage capacity and heat transmit delay characters are studied. Then, the consumption capacity of surplus wind power is discussed, and the temperature changes are also be considered. Finally, a case study are undertook to prove the effectiveness. The results show that by utilizing the heat in heating network, the wind power curtailment rate could be reduce by 20.6% which means about 219.4MWh surplus wind energy could be consumed during a valley electric load period.

Increasing the application of renewable energy in the power system is an effective way to achieve the goal of ‘Dual Carbon’. At the same time, the high proportion of renewable energy connected to the grid endangers the safe operation of the power system. To solve this problem, this paper proposes the application of a copula function to describe the correlation between wind power and photovoltaic power, and reduce the uncertainty of power-system operation with a high proportion of renewable energy. In order to increase the robustness of the model, this paper proposes the application of the conditional value-at-risk theory to construct the objective function of the model and effectively control the tail risk of power-system operation costs. Through case analysis, it is found that the model proposed in this paper has strong practicality and economy.

For high-proportional wind power access to power grids, how to coordinate the energy storage switching strategies with other various power generation energy sources is the key to the effective utilization of energy storage and the improvement of wind power consumption. This paper proposed a safety barrier that power grid established through energy storage for eliminating wind power, studied evaluation and identification methods of energy storage input, and established the model of energy storage operation and the relationship between energy storage capacity and wind power consumption demand energy storage capacity and clean energy consumption demand. It also constructed a method for analyzing the correlation between electric heating thermal storage load and abandonment wind during heating period, and explored the qualitative relationship between abandoned wind power, abandoned wind hours and abandoned wind power. However, the energy storage capacity is configured according to the maximum abandonment power, and the energy storage efficiency is not high, and the wind power that is consumed by the electric heating thermal storage load is the abandoned wind power during the heating period.

In recent years, with a rapid growth of the generation of hydropower, wind and solar power, renewable energy has occupied more than 60% of the total installed capacity of power grids in Qinghai, Yunnan, Sichuan, Tibet and other areas. Along with the yearly increased power transmission from these provinces, the ultra-high voltage AC/DC hybrid power grids will be evolving to be the multi-DC asynchronous grid, which is a trend of future represented by the work done in Yunnan. Taking Yunnan province as an example, this article studies the operational problems related to the multi-DC asynchronous power-sending grid with high proportion of renewable energy, and describes a new secure and stable operational technology that has been applied into the grid in Yunnan. We case-study the effects of the operation after the application of such technology and evaluate its practical influences.

Research on peak load shifting for hybrid energy system with wind power and energy storage based on situation awareness

Compared with other conventional energy sources, renewable energy, which results in less pollution and has numerous resources, is a significant factor in addressing the current issues of the serious environmental pollution and the resource depletion. Large-scale renewable energy connected to the grid could lead to a the change of the morphological structure and the operation modes of energy transmission. While current power grid evolution models did not consider the characteristics of renewable energy in the evolution strategy. The expansion mechanism of the future transmission network with a high proportion of renewable energy is studied in this paper. The operation cost including renewable energy curtailment are optimized using the production simulations, where the stochastic and intermittent of wind and solar energy are modeled. And the dual variable information is adopted to drive the grid upgrading. The expansion strategies for power grid of Zhejiang province in China are analyzed to balance the construction cost and curtailment cost of renewable energy.

Wind power ultra-short-term prediction method based on NWP wind speed correction and double clustering division of transitional weather process

A regulation capability enhancing strategy for controllable loads based on relay control

Electric heating load forecasting of power system plays an important role in the safe and economic stability based on historical data is the key feature of regression tree model and based on the behavior characteristics, building electrical heating load forecasting model, through the change of the external electricity price incentives, to obtain dynamic characteristic of the electric heating load data, access to electricity heating static characteristic and dynamic characteristic data of variation, According to the variation of dynamic response, the application value of dynamic characteristics of electric heating load on the power grid side is analyzed. Data analysis shows that the method has good prediction accuracy and potential application of dynamic characteristics.

Wind power is developing rapidly in the context of sustainable development, and a series of problems such as wind curtailment and power curtailment have gradually emerged. The forecast of power generation output has become one of the hotspots of current research. This paper proposes a wind power plant output ultra-short-time prediction technology based on variational modal decomposition and particle swarm optimization least squares vector machine. Variational Modal Decomposition (VMD) method decomposes the historical output data of wind power plants at multiple levels. At the same time, it explores the impact of various decomposition methods such as EMD decomposition on the prediction accuracy, and uses the least squares support vector machine based on particle swarm optimization algorithm. Predictive summation is performed on each level of data separately to obtain a more accurate prediction effect, which has a certain improvement in prediction accuracy compared with traditional prediction algorithms.

The high proportion of renewable energy into the power grid, say, the large-scale wind power generation connected to the power grid, is the key approach to achieve the clean and low-carbon energy transition. Under the premise of ensuring power grid's safe and stable operation, it is necessary to study the influence of wind power capacity expansion on the transient angle stability (TAS). Aiming at several typical dominant modes in Qinghai power system, which holds many new energy resources, the sensitivity of wind power capacity expansion in different locations to the TAS, as well as its key influence factors and feasible explanations, are explored. It may lay the foundation of the location optimization of wind power capacity expansion.

Denmark is a small Scandinavian country with a land area of 43,000 km2 and a population of about 5.5 million, and it is known for its high proportion of renewable energy for energy consumption. In the past 20 years, the proportion of total energy generated from renewable sources has increased from approximately 5 per cent to 17 per cent. In 2007, renewable energy production was calculated to be 130.2 petajoules (PJ), with wind power and biomass production accounting for 25.8 PJ and 90.5 PJ, respectively (see Figure 4.5.1). In biomass production, straw accounted for 18.3 PJ, wood 41.2 PJ, renewable waste 30.1 PJ and fish oil 0.8 PJ. In 2007, wind power accounted for 19.7 per cent of domestic electricity supply, a significant increase from only 1.9 per cent in 1990 (Danish Energy Agency, 2008). Therefore, the Danish government established a target of increasing the share of renewable energy to at least 30 per cent of total energy consumption by 2025 (Danish Energy Authority, 2007).

The integration of a high proportion of renewable energy has significantly reduced the grid inertia level and markedly increased the risk of transient frequency instability in power systems. Meanwhile, the large-scale integration of diverse heterogeneous resources-such as wind power, photovoltaics, energy storage, and high voltage direct current (HVDC) transmission systems-has considerably enriched the portfolio of frequency regulation assets in modern power grids. However, the marked disparities in the dynamic response characteristics and actuation speeds among these resources introduce significant nonlinearity and high-dimensional complexity into the system's transient frequency behavior. As a result, conventional methods face considerable challenges in achieving accurate and timely prediction of such responses. However, the substantial differences in the frequency regulation characteristics and response speeds of these resources have led to a highly nonlinear and high-dimensional complex transient frequency response process, which is difficult to accurately and rapidly predict using traditional methods. To address this challenge, this paper proposes an online prediction method for transient frequency response that deeply integrates physical principles with data-driven approaches. First, a frequency dynamic response analysis model incorporating the frequency regulation characteristics of multiple resource types is constructed based on the Single-Machine Equivalent (SME) method, which is used to extract key features of the post-fault transient frequency response. Subsequently, information entropy theory is introduced to quantify the informational contribution of each physical feature, enabling the adaptive weighted fusion of physical frequency response features and Wide-Area Measurement System (WAMS) data. Finally, a physics-guided machine learning framework is proposed, in which the weighted physical features and the complete frequency curve predicted by the physical model are jointly embedded into the prediction process. An MLP-GRU-Attention model is designed as the data-driven predictor for frequency response. A physical consistency constraint is incorporated into the loss function to ensure that predictions strictly adhere to physical laws, thereby enhancing the accuracy and reliability of the transient frequency prediction model. Case studies based on the modified IEEE 39-bus system demonstrate that the proposed method significantly outperforms traditional data-driven approaches in terms of prediction accuracy, generalization capability under small-sample conditions, and noise immunity. This provides a new avenue for online frequency security awareness in renewable-integrated power systems with multiple heterogeneous frequency regulation resources.