Chongqing Kang

Unit Commitment With Volatile Node Injections by Using Interval Optimization

In response to the challenges brought by uncertain bus load and volatile wind power to power system security, this paper presents a novel unit commitment formulation based on interval number optimization to improve the security as well as economy of power system operation. By using full-scenario analysis, the worst-case impact of volatile node injection on unit commitment is acquired, so that the proposed model can always provide a secure and economical unit commitment result to the operators. Scenarios generation and reduction method based on interval linear programming theory are used to accelerate the solution procedure without loss of optimality. Benders decomposition is also implemented to reduce the complexity of this large-scale interval mixed integer linear programming, and prove the rationality and rigor of our proposed method. The numerical results indicate better secure and economical features of the proposed method comparing with the traditional one.

Power Generation Expansion Planning Model Towards Low-Carbon Economy and Its Application in China

Climate change poses a huge threat to human welfare. Hence, developing a low-carbon economy has become a prevailing and inevitable trend. Decarbonization of power generation, especially converting the current power mix into a low-carbon structure, will be a critical option for CO2 emission mitigation. In this paper, an integrated power generation expansion (PGE) planning model towards low-carbon economy is proposed, which properly integrates and formulates the impacts of various low-carbon factors on PGE models. In order to adapt to the characteristics of PGE models based on low-carbon scenario, a compromised modeling approach is presented, which reasonably decreases complexities of the model, while properly keeping the significant elements and maintaining moderate precision degree. In order to illustrate the proposed model and approach, a numerical case is studied based on the background of Chinas power sector, making decisions on the optimal PGE plans and revealing the prospects and potentials for CO2 emission reduction.

Increasing the Flexibility of Combined Heat and Power for Wind Power Integration in China: Modeling and Implications

With the largest installed capacity in the world, wind power in China is experiencing a ~ 20% curtailment during operation. The large portion of the generation capacity from inflexible combined heat and power (CHP) is the major barrier for integrating this variable power source. This paper explores opportunities for increasing the flexibility of CHP units using electrical boilers and heat storage tanks for better integration of wind power. A linear model is proposed for the centralized dispatch for integrated energy systems considering both heat and power, with detailed modeling of the charging processes of the heat storage tanks. The model balances heat and power demands in multiple areas and time periods with various energy sources, including CHP, wind power, electrical boilers, and heat storage. The impact of introducing electrical boilers and heat storage systems is examined using a simple test system with characteristics similar to those of the power systems in Northern China. Our results show that both electrical boilers and heat storage tanks can improve the flexibility of CHP units: introducing electrical boilers is more effective at reducing wind curtailment, whereas heat storage tanks save more energy in the energy system as a whole, which reflect a different heating efficiency of the two solutions.

Modeling Conditional Forecast Error for Wind Power in Generation Scheduling

The integration of wind power requires additional operating reserves to cope with the uncertainty in power system operation. Previous research shows that the uncertainty of the wind power forecast varies with the level of its output. Therefore, allocating reserves dynamically according to the specific distribution of the wind power forecast would benefit system scheduling. This paper presents a statistical model to formulate the conditional distribution of forecast error for multiple wind farms using copula theory. The proposed model is tested using a set of synchronous data of wind power and its day-ahead forecast. It is then utilized in a stochastic unit commitment model to simulate the day-ahead and real-time scheduling of the modified IEEE RTS-79 system integrating wind power. The results show that scheduling reserves dynamically according to the modeled conditional forecast error reduces the probability of reserve deficiency while maintaining the same level of operating costs.

Planning Pumped Storage Capacity for Wind Power Integration

Pumped storage can provide some of the flexibility that power system operators need to balance load and generation in an uncertain environment, and thus enhance a power systems ability to incorporate wind power. Since the process of balancing wind power involves various combinations of wind generation and loads, the amount of pumped storage capacity needed should be evaluated using a substantial number of scenarios. This paper describes a chronological production simulation platform and its application in planning pumped storage capacity for the Jiangsu (China) provincial power system. The daily dispatching of various types of units is simulated using a unit commitment module. A simulation of wind farm operation is incorporated in this module to take into account the effect of its variability on daily dispatching. A detailed cost model for thermal generating units provides an accurate estimate of the benefits of pumped storage. Simulation results clearly show how much generation cost and wind power curtailment should be expected for different amounts of pumped storage capacity. A comparison between the operating and investment costs is then used to determine the optimal pumped storage capacity. Finally, various sensitivity analyses are performed to assess the effect of key parameters on this optimal capacity.

Optimal Bidding Strategy of Battery Storage in Power Markets Considering Performance-Based Regulation and Battery Cycle Life

Large-scale battery storage will become an essential part of the future smart grid. This paper investigates the optimal bidding strategy for battery storage in power markets. Battery storage could increase its profitability by providing fast regulation service under a performance-based regulation mechanism, which better exploits a batterys fast ramping capability. However, battery life might be decreased by frequent charge-discharge cycling, especially when providing fast regulation service. It is profitable for battery storage to extend its service life by limiting its operational strategy to some degree. Thus, we incorporate a battery cycle life model into a profit maximization model to determine the optimal bids in day-ahead energy, spinning reserve, and regulation markets. Then a decomposed online calculation method to compute cycle life under different operational strategies is proposed to reduce the complexity of the model. This novel bidding model would help investor-owned battery storages better decide their bidding and operational schedules and investors to estimate the battery storages economic viability. The validity of the proposed model is proven by case study results.

A Convex Model of Risk-Based Unit Commitment for Day-Ahead Market Clearing Considering Wind Power Uncertainty

The integration of wind power requires the power system to be sufficiently flexible to accommodate its forecast errors. In the market clearing process, the scheduling of flexibility relies on the manner in which the wind power uncertainty is addressed in the unit commitment (UC) model. This paper presents a novel risk-based day-ahead unit commitment (RUC) model that considers the risks of the loss of load, wind curtailment and branch overflow caused by wind power uncertainty. These risks are formulated in detail using the probabilistic distributions of wind power probabilistic forecast and are considered in both the objective functions and the constraints. The RUC model is shown to be convex and is transformed into a mixed integer linear programming (MILP) problem using relaxation and piecewise linearization. The proposed RUC model is tested using a three-bus system and an IEEE RTS79 system with wind power integration. The results show that the model can dynamically schedule the spinning reserves and hold the transmission capacity margins according to the uncertainty of the wind power. A comparison between the results of the RUC, a deterministic UC and two scenario-based UC models shows that the risk modeling facilitates a strategic market clearing procedure with a reasonable computational expense.

Secondary Forecasting Based on Deviation Analysis for Short-Term Load Forecasting

Short-term load forecasting (STLF) is the basis of power system planning and operation. With regard to the fast-growing load in China, a novel two-stage hybrid forecasting method is proposed in this paper. In the first stage, daily load is forecasted by time-series methods; in the second stage, the deviation caused by time-series methods is forecasted considering the impact of relative factors, and then is added to the result of the first stage. Different from other conventional methods, this paper does an in-depth analysis on the impact of relative factors on the deviation between actual load and the forecasting result of traditional time-series methods. On the basis of this analysis, an adaptive algorithm is proposed to perform the second stage which can be used to choose the most appropriate algorithm among linear regression, quadratic programming, and support vector machine (SVM) according to the characteristic of historical data. These ideas make the forecasting procedure more accurate, adaptive, and effective, comparing with SVM and other prevalent methods. The effectiveness has been demonstrated by the experiments and practical application in China.

Load profiling and its application to demand response: A review

The smart grid has been revolutionizing electrical generation and consumption through a two-way flow of power and information. As an important information source from the demand side, Advanced Metering Infrastructure (AMI) has gained increasing popularity all over the world. By making full use of the data gathered by AMI, stakeholders of the electrical industry can have a better understanding of electrical consumption behavior. This is a significant strategy to improve operation efficiency and enhance power grid reliability. To implement this strategy, researchers have explored many data mining techniques for load profiling. This paper performs a state-of-the-art, comprehensive review of these data mining techniques from the perspectives of different technical approaches including direct clustering, indirect clustering, clustering evaluation criteria, and customer segmentation. On this basis, the prospects for implementing load profiling to demand response applications, price-based and incentive-based, are further summarized. Finally, challenges and opportunities of load profiling techniques in future power industry, especially in a demand response world, are discussed.

Modeling Flexible Operation Mechanism of

CO2 capture and storage (CCS) has been identified as a critical and promising option for power generation in a carbon-constrained world. Flexible-operation mechanism of CO2 capture power plant is of great significance no matter in enhancing economic returns of plant investment or in ensuring the secure operation of power system. In this paper, the feasibility, mechanism, and options of flexible operation in capture plant are first clarified. Then, based on a benchmark capture plant with postcombustion and solvent/sorbent separation technology, a generic quantitative model is established to formulate the process of CO2 capture and the interaction between capture system and generation system. Plant performances as well as its effects on power-system operation are examined, revealing the different characteristics between CO2 capture power plant and conventional noncapture plants. On this basis, typical operation modes of CO2-capture power plant are defined and identified. In the end, a numerical case is studied to testify the effectiveness of the proposed model.