Reduce Wind Power Curtailment Research Articles

A collaborative planning model of IES containing EHs and multi-energy grids under the premise of demand response as well as wind power uncertainty

The proliferation of renewable energy resources and the incorporation of various types of energy bring opportunities as well as new challenges to the planning and operations within integrated energy systems (IESs). To better characterize multiple uncertain variables within IES, reducing wind power curtailment and minimizing costs, this paper proposes an IES collaborative planning model with EHs, in which uncertain variables are depicted by Z-numbers. Firstly, Z-number incorporates both fuzzy and probabilistic uncertainties, allowing the representation of uncertain variables with credibility information in constraints. Secondly, simplifying Z-numbers into an engineering-applicable method reduces the complexity of model resolution. Thirdly, to fully leverage the complementary transformation characteristics among different energy sources, the optimization model includes decisions on the capacity of energy conversion and energy storage components within EHs. Moreover, outside the EHs, conventional generator dispatches, and the hardening of electric-gas grid decisions are also included. Finally, the model is verified on an IES that involves the coupling of a six-node power system and a six-node gas system, and numerical results validate that wind power output values represented by Z-numbers have a standard deviation of only 0.33 from actual values. Furthermore, different credibility information in Z-numbers significantly impacts planning outcomes; an increased DR credibility can be conducive to reducing wind curtailment with 45KW and help cost-savings with 4.05%.

Optimal Bidding Strategies for Wind-Thermal Power Generation Rights Trading: A Game-Theoretic Approach Integrating Carbon Trading and Green Certificate Trading

In response to the challenges of low wind power consumption and high pollution emissions from thermal power, the implementation of wind-thermal power generation rights trading is a proactive attempt to reduce wind power curtailment and promote its consumption. This study first regards the alternating bidding process between the two parties as a dynamic game, using the Rubinstein bargaining game model to determine the incremental profit allocation and optimal bidding for both parties in power generation rights trading. Secondly, an energy conservation and emission reduction model is constructed to analyze the benefits from the perspectives of standard coal consumption saving and the carbon emission reduction caused by power generation rights trading. Finally, a combined trading revenue model is established to analyze the final profit of both parties involved in the trading. The results show that the combined trading of wind-thermal power generation rights, incorporating carbon trading and green certificate trading, can effectively promote coal consumption savings in thermal power units and reduce the carbon emissions of the power industry. Moreover, it significantly increases the final profit for both parties, stimulating the enthusiasm of generators for participating in power generation rights trading, and ultimately promoting wind power consumption.

Security assessment and preventive control of combined electricity and heat systems with distributed electric thermal storage

Achieving carbon peak and neutrality targets can be accelerated by reducing wind power curtailment (WPC). However, WPC is constrained due to the electric thermal coupling characteristics of combined heat and power. To tackle this challenge, we propose an optimal scheduling framework for combined electricity and heat systems (CEHS) with distributed electric thermal storage (DETS) and perform security assessment and preventive control. Firstly, a general model is proposed to schedule DETS for different types of customers. The customer flexibility regulation variable (CFRV) for each customer is developed to fill this knowledge gap, and the excess thermal storage stored in DETS is used for auxiliary thermal network regulation (ATNR). Secondly, a two-stage security assessment and preventive control method for CEHS with DETS is proposed to correct overloaded branch flow. Thirdly, an evaluation index is developed to evaluate the severity of overloaded branches. The results show that the method of considering CFRV can reduce WPC and operation costs compared to the traditional method. Adopting ATNR enhances the flexibility and economics of grid scheduling compared to non-ATNR. Moreover, assessments of overloaded branches in various cases show that the preventive control method mitigates overload risks.

System Optimization and Operating Strategy of Single-Stage Air Source Heat Pump with Thermal Storage to Reduce Wind Power Curtailment

Wind power generation has increased in China to achieve the target of decreasing CO2 emissions by 2050, but there are high levels of wind curtailment due to the mismatch between electricity supply and demand. This paper proposes a single-stage air source heat pump coupled with thermal storage for building heating purposes. The main objective is to find the proper system designs and operating strategy, which can help to avoid peak demand periods while obtaining minimized running costs and reduced wind energy curtailment. Dynamic simulations were performed using TRNSYS to investigate its use in a typical office building based on an actual electricity tariff, wind power, and meteorological data. The proper system designs, including the tank size and thermal storage temperature, were determined to maximize the system’s performance. It was found that a proper combination of the two parameters exists for a specific application. Further, results showed that the use of auxiliary electric heating is necessary for single-stage air source heat pumps to participate in a wind curtailment reduction. The operating strategy of the system was also studied. Results indicate that by implementing a proper operating strategy, non-renewable power consumption can be reduced by 11% for the studied building, with a total wind power utilization of 3348 kWh during the heating season while still satisfying the heating demands of users. These findings can contribute to the green and low-carbon development of the building industry and further enhance the grid’s accommodation capacity for renewable energy sources.

Winding down the wind power curtailment in China: What made the difference?

The carbon neutrality goal requires significant acceleration of the renewable energy transition. In China, this acceleration is hampered because of the concerns regarding the recent high-rate wind power curtailment. However, post-2016, wind power curtailment shows notable reductions. Therefore, it is vital to understand the fundamental factors driving such improvements, the curtailment reduction patterns across the countries and key regions, the experiences from the regional heterogeneity of curtailment reductions, and the policy implications available to strengthen wind power curtailment mitigation strategies in the short and long term to accelerate China's clean energy transition. This study constructs an evaluation framework based on the logarithmic mean Divisia index approach to investigate these concerns. Furthermore, it reveals that the eminent contributors at the national scale are local power demand, power exports, and power structures. However, the regional factor patterns oscillate significantly. In northwest China, power transmission is vital to reduce wind power curtailment. In north China, thermal power remains dominant because of its importance to national energy security, impeding its curtailment. Northeast China implements the peak-shaving auxiliary service market, promoting the power grid's capability to consume more local wind power. The fundamental settlement of the curtailment issue calls for market-oriented energy structure reforms to ensure sustainable low-carbon development. This study explains the essence of China's renewable energy development and seeks reliable paths to accelerate wind power integration with policy measures and technical transformations to enhance the adaptability of the power grid system.

An Optimal Dispatch Framework of Electric and Heating Networks Based on Controllable Electric and Thermostatically Controlled Loads

With the increasing capacity of wind power generators (WTGs), the volatility of wind power could significantly challenge the stability and economy of electric and heating networks. To tackle this challenge, this paper proposes an optimal dispatch framework based on controllable load (including controllable electric load and controllable thermostatically load) to reduce wind power curtailment. A forecasting model is developed for the controllable load, which comprehensively considers autocorrelation, weather factor, and consumers’ behavior characteristics. With adjusting controllable load, an optimal dispatch model of power system is then established and resolved by Sequential Least Squares Programming (SLSQP) method. Our method is verified through numerous simulations. The results show that, compared with the state-of-the-art techniques of support vector machine and recurrent neural networks, the root mean square error with the proposed long short-term memory can be reduced by 0.069 and 0.044, respectively. Compared with conventional method, the peak wind power curtailment with dispatching controllable load is reduced by nearly 10% and 5% in two cases, respectively.

Reducing human health impacts from power sector emissions with redispatch and energy storage

Emissions from the power sector significantly contribute to ambient air pollution and its associated adverse human health impacts. In this study, we explore how to cost-effectively reduce health impacts due to fine particulate matter (PM2.5) attributable to power plant emissions by internalizing real-time health costs in plant dispatch decisions and re-optimizing the unit commitment and economic dispatch in light of these impacts. We show that internalizing the time- and location-varying health damage costs into power system operational decisions can reduce 61%–97% of adverse health impacts through decreases in coal generation and strategic shifts in the location and timing of pollutant releases. We also find that adding energy storage to the grid can mitigate health impacts by reducing wind power curtailment. Our findings demonstrate the need to consider temporal and spatial heterogeneity when determining the social cost of emissions.

Locational Marginal Pricing and Daily Operation Scheduling of a Hydro-Thermal-Wind-Photovoltaic Power System Using BESS to Reduce Wind Power Curtailment

The Daily Operation Scheduling (DOS) gets new challenges while a large-scale of renewable energy is inserted into the power system. In addition to the operation, the power variability of these sources also causes a problem in the hourly pricing, represented here by Locational Marginal Pricing (LMP). Therefore, new applications, such as energy shifting, offer greater efficiency to the system, minimizing the negative effects caused by wind power curtailment (WPC). This paper shows the LMP formation in the DOS of the hydro-thermal-wind-photovoltaic power system with a battery energy storage system and the reduction of WPC. Here, the wind and photovoltaic power plants are designed to be dispatched, not mandatory, to be able to cut the generation, and the insertion of Distributed Generation is considered. Moreover, to solve the DOS problem, the interior-point method is used. Additionally, the DC optimal power flow, used to represent the DOS in addition to the representation of the electric grid, is modeled with an iterative approach. The analysis is made in an IEEE 24-bus system with data from Brazil. Lastly, the results of simulations are presented and discussed, demonstrating the effectiveness of the optimization to reduce the WPC, the total operation cost, and to provide the LMP curve.

Data-driven stochastic unit commitment considering commercial air conditioning aggregators to provide multi-function demand response

This paper proposes a two-stage data-driven unit commitment dispatching scheme, which coordinates the multi-function demand response of commercial air conditioning aggregators with thermal units to encounter the variations of load and wind power. In the proposed dispatching scheme, the flexible ramping capability of the thermal units is reflected in the coupling constraints between the ramping capacity and reserve capacity. To improve the flexibility of the whole system, multi-function demand response of commercial air conditioning aggregators is incorporated into the scheduling in both stages. In the day-ahead stage, load-shifting demand response reduces the ramping requirements by reshaping the load profile. In the real-time stage, reserve-capacity demand response releases the ramping capability by providing reserve for the rebalance of wind power fluctuation, which is represented by a data-driven uncertainty set. Since the dispatching scheme is formulated as a min–max-min problem, an accelerating solution method is developed to improve the computational efficiency, which combines the column-and-constraints generation algorithm and the specific property of the model. The simulation results tested on the modified IEEE 118-bus system and Henan power grid in China validate the effectiveness of the dispatching scheme and solution method in increasing flexible ramping capability, reducing wind power curtailment and load shedding, improving economic efficiency, and accelerating computation.

Multi-Objective Optimization Strategy of Integrated Electric-Heat System Based on Energy Storage Situation Division

There are the transmission loss of the electric power network, the delay and loss of the heating network, the insufficient utilization of flexible resources such as energy storage in the integrated electric-heat system, which may lead to the imbalance of supply and demand and energy waste. In this paper, the coordinated dispatch of integrated electric-heat system (IEHS) considering the transmission characteristics of the electric power network and heating network, which is formulated as a convex quadratic program. The strong linkage of electric power and heat supplies can be decoupled to reduce wind power curtailment by exploiting the energy storage and regulation capabilities of the district heating network (DHN), storage batteries, electric boilers (EBs) and heat storage tanks (HSs). The energy storage system works according to the situation division strategy designed in this paper. This paper introduces the wind curtailment boundary power and optimizes dispatch based on the wind curtailment boundary power and unit output, which can make full use of the energy storage capacity and reduce the wind abandonment power. Since the electric power system (EPS) and the distribution heating system (DHS) are controlled separately by different operation organizations, IEHS is solved using double-λ iterative algorithm. The double-λ iterative algorithm, with guaranteed convergence for convex programs, can achieve a fully distributed solution for the IEHS and requires only a small amount boundary information exchange between the EPS and the DHS. At last, one integrated electric-heat system was studied to demonstrate the effectiveness of the proposed method which achieves the effective solution in a moderate number of iterations. This system includes two 10-nodes heating system and one 14-nodes electric power system.

Exploiting flexibility of combined-cycle gas turbines in power system unit commitment with natural gas transmission constraints and reserve scheduling

With the integration of large-scale renewable energy including wind power into the power grid, efficient flexible resources are required for wind power accommodation. The combined-cycle gas turbines (CCGTs) are widely used in the power system with flexible operation characteristics, which leads to enhanced interdependencies between the power grid and the natural gas network. In this paper, a unit commitment (UC) model with natural gas transmission constraints and reserve scheduling considering the flexibility of CCGT with quick startup-shutdown and flexible operation characteristics is proposed, to reduce wind power curtailment and high startup and shutdown cost of conventional generation units in the power system. Considering the complex operating conditions of CCGT and the non-convexity of Weymouth equation in natural gas network, this paper adopts an improved mode-based model (MBM) for modeling of CCGT and converts the Weymouth equation into a second-order cone relaxation (SOCR) formulation with penalty term through a concave-convex process (CCP) to make the relaxation tight, thus transforming the UC model into an iterative mixed-integer second-order cone programming (MISOCP) problem to be efficiently solved. The effectiveness of the proposed model on reducing wind power curtailment and startup-shutdown cost is demonstrated through case studies.

Reducing wind power curtailment by risk-based transmission expansion planning

Due to the inherent intermittency of wind power and the shortage of transmission capacity, a considerable amount of wind power cannot be delivered to load centres. The resulting curtailment of wind power poses a serious challenge to transmission planners. In this paper, we take advantages of the superquantile theory and formulate a risk-based transmission expansion planning (TEP) model to reduce the curtailment of wind power. Regarding its non-convexity and computation difficulty induced by increasing scenarios, we propose a convex relaxation method that can greatly improve computational efficiency. In the numerical study, the computational efficiency of the proposed solution strategy is verified. The comparable results demonstrate the effectiveness and superiority of the risk-based TEP model in terms of making a trade-off between the curtailment of wind power and the transmission investment cost.

Dynamic energy conversion and management strategy for an integrated electricity and natural gas system with renewable energy: Deep reinforcement learning approach

With the application of advanced information technology for the integration of electricity and natural gas systems, formulating an excellent energy conversion and management strategy has become an effective method to achieve established goals. Differing from previous works, this paper proposes a peak load shifting model to smooth the net load curve of an integrated electricity and natural gas system by coordinating the operations of the power-to-gas unit and generators. Moreover, the study aims to achieve multi-objective optimization while considering the economy of the system. A dynamic energy conversion and management strategy is proposed, which coordinates both the economic cost target and the peak load shifting target by adjusting an economic coefficient. To illustrate the complex energy conversion process, deep reinforcement learning is used to formulate the dynamic energy conversion and management problem as a discrete Markov decision process, and a deep deterministic policy gradient is adopted to solve the decision-making problem. By using the deep reinforcement learning method, the system operator can adaptively determine the conversion ratio of wind power, power-to-gas and gas turbine operations, and generator output through an online process, where the flexibility of wind power generation, wholesale gas price, and the uncertainties of energy demand are considered. Simulation results show that the proposed algorithm can increase the profit of the system operator, reduce wind power curtailment, and smooth the net load curves effectively in real time.

Alterable Electricity Pricing Mechanism Considering the Deviation of Wind Power Prediction

Fluctuation and prediction errors of wind power would cause a large amount of automatic generation control (AGC) adjustment costs, which lead to the problem of power curtailment. A reasonable mechanism of grid-connection electricity price may encourage wind farms to take measures to reduce the deviation between output power and schedule power, which is helpful for source-network coordination and reducing wind power curtailment. An alterable electricity pricing mechanism considering wind power deviation rate is proposed. In each schedule cycle, electricity price is adjusted according to the deviation rate and its historical change trend. In this way, wind farms will be encouraged to configure energy storage to promote the accordance of wind output power with schedule power to the greatest extent. Given the statistical characteristic of prediction errors of wind power, this paper proposes a schedule power model, taking least squares of output power deviation as objective function, and then puts forward an engineering application method for determining schedule power. This paper analyzes the overall cost and revenue of a wind farm to configure energy storage and determine the optimal energy storage capacity with the goal of maximizing the profit of the wind farm. In the case analysis, the effect of the deviation rate and its historical change trend, the deviation rate tolerance coefficient on electricity price is analyzed. The case analysis demonstrates the effectiveness of the proposed alterable electricity pricing mechanism and shows that the mechanism is helpful at reducing wind power output deviation and wind curtailment.

Optimal operation of flexible heating systems for reducing wind power curtailment

Flexibility in a heating system refers to the ability of regulating its heat consumption/supply/storage from time to time, which indirectly affects the corresponding electricity production and consumption. Through an optimal use this kind of ability, integration challenges for wind power such as curtailment can be addressed. In the paper, a series of flexibility options in the heating system are modeled and investigated, including extraction combined heat and power plant, electric boiler, electric heat pump (HP), heat storage and demand response management of the heat load. Based on a mathematical model developed for achieving an optimal dispatch of the heating system, heating systems with various configurations of flexibility options can be investigated regarding their contribution to wind power curtailment. A practical case study is presented to validate the proposed solution, with eight configuration scenarios defined to represent the existing and other possible system setups. The results show that there is a huge potential of using the flexibility from the heat sector to support wind power integration, and the potential can be affected by the seasonal variations of energy demand, wind profile and technical parameters like efficiency of the HP. Further, contribution of different flexibility options to wind power integration is different in regards to the achieved operation-scale techno-economic performance.

Robust Two-Stage Regional-District Scheduling of Multi-carrier Energy Systems With a Large Penetration of Wind Power

This paper proposes a robust day-ahead scheduling method for a multi-carrier energy system (MES), which would enhance the flexibility of power systems with a large sum of variable wind power. We build an MES model and propose an optimal MES schedule which helps MES reduce wind power curtailment in power systems. At first, electricity and natural gas networks are coordinated at the transmission (regional) level for accommodating the large penetration of wind power in regional MES. The distribution (district) level MES coordinates energy conversion and storage to jointly supply the electricity, natural gas, and heat loads. The transmission level MES is modeled using detailed network equations while the distribution level MES is modeled as a device with multiple input/output ports using the linear branch-flow-based energy hub model. A two-stage robust model is established to consider the variability of wind power at the two MES levels. The proposed problem is solved by a nested column-and-constraint (C&CG) generation method. The first-stage problem which schedules the hourly unit commitment is solved in the outer loop, while the inner loop solves the second-stage problem to realize the worst scenario. Several acceleration strategies are utilized to enhance the computational performance of the nested C&CG. Numerical results offered for a 6-bus 3-node system and a modified IEEE 118-bus 10-node system show the effectiveness of the proposed MES model and solution technique for enhancing the power system flexibility.

Joint Commitment of Generation Units and Heat Exchange Stations for Combined Heat and Power Systems

The combined heat and power system introduces a higher efficiency in energy conversion and consumption. By exploiting the flexibility of district heating systems (DHSs), the joint operation of heat and power systems can improve the overall system flexibility, reduce renewable energy curtailment, and decrease system operating costs. In a typical DHS, the heat exchange station (HES) is a key component which can help adjust the heat distribution among heat loads. In this paper, a joint hourly commitment of generation units and HESs is proposed. The DHS model is presented in which thermal storage and inertia of pipelines and heat loads are characterized. In addition, an approximation is applied to the HES model, making the overall joint commitment problem tractable. Numerical simulations are carried out, which demonstrate that the proposed joint commitment solution can introduce additional benefits in reducing wind power curtailment and system operation cost.

Optimal bidding strategy for wind farms considering local demand response resources

The negative impacts imposed by wind power on power systems can be decomposed into different time scales. Demand response (DR) can relieve these impacts by providing different auxiliary services. However, based on the current integration requirement, wind farms cannot earn more even if they try to provide power with less negative impacts. In this regard, three indicators are proposed for three different time scales to evaluate these efforts of wind power resources. The system operator sets the purchasing price of wind power by using these indicators. It aims to encourage wind farms to improve their output curves by purchasing the DR supporting services products. In the next step, a bi-level optimisation model is developed to assist the wind farms in their bidding and DR purchasing strategies. The case studies show that the proposed model can reduce wind power curtailment and improve the power's features. Moreover, by using the indicators as the integration requirement, the revenue of wind farms and the economic benefit of system dispatch are improved.

Economic dispatch model for wind power integrated system considering the dispatchability of power to gas

Power-to-gas (P2G) technology opens up another perspective for effective, long-term, large-scale energy storage, so that it can promote large-scale wind power integration. Under this background, this study introduces P2G technology into the economic dispatch model of wind power integrated systems. First, an uncertainty interval model of wind power is built based on the probability distribution of wind power prediction error. Then, considering P2G's function of absorbing surplus wind power and undertaking downward spinning reserve, the factors that influence P2G's ability in enhancing the accommodation of wind power are analysed through theoretical derivation. Consequently, the relationship between the maximum wind power integration and the power delivered to the P2G facilities under a given capacity of P2G can be further understood. Moreover, an economic dispatch model incorporating wind power, P2G facilities and battery energy storage systems is established and a feasible region relaxation algorithm is proposed to deal with the non-smooth functions in the proposed model. Case studies are conducted on a modified IEEE 39-bus system. The results verify the correctness of theoretic analysis and show that the proposed model can help to reduce wind power curtailment and enhance the security and economy of the power system.

Day‐ahead scheduling of integrated electricity and district heating system with an aggregated model of buildings for wind power accommodation

In traditional integrated electricity and district heating systems, the inflexible operation of combined heat and power units leads to a large amount of wind power curtailments during winter. The thermal inertia of aggregated buildings can provide additional operational flexibility to promote wind power accommodation. In this study, a day-ahead scheduling model for integrated electricity and district heating system considering the thermal inertia of buildings is proposed. In this work, the operation model of the district heating network under constant mass flow and variable temperature operation strategy is presented, and the aggregated model of buildings based on the detailed thermal model of buildings is established. Then, the scheduling framework is analysed and the day-ahead scheduling model is formulated as quadratic programming problem to minimise the operation cost of integrated electricity and district heating system. The validity of the proposed model is verified by the case studies performed on a 6-bus power system with a 6-node heating system and IEEE 39-bus electricity system with a 16-node heating system. The results demonstrate that the thermal inertia of buildings can provide additional operational flexibility and effectively help reduce wind power curtailment and operation costs.