Power and Energy Resources

Inhaltsverzeichnis

1. Frontmatter

2. Statistically Feasible Robust Power System Operation

   Wenqian Jiang, Jian Shi, Chenye Wu

   Abstract

   The escalating threat of climate change is driving the electric power system through a profound transition toward net-zero emissions. During this transition, the most noticeable characteristic is the large-scale integration of renewable generation sources, which has increased almost fourfold over the past ten years (from 6% to more than 30%). Although renewable energy brings clean power, its inherent uncertain generation leads to potential power grid instability. Hence, dealing with the newly integrated uncertainty in modern power system operations is vital. Classical approaches to handling uncertainties include chance-constrained programming and robust optimization. However, chance-constrained programming is effective often when the distribution knowledge is accessible, whereas solutions from robust optimization are often criticized for conservatism. To this end, distributionally robust optimization improves robust optimization and chance-constrained programming by considering distributional misspecification. Nevertheless, the misspecification may also arise during dataset collection. Intuitively, sample sets with different quality will lead to diverse estimations for uncertainties. This warrants considering the dataset collection misspecification issue in detail, which has only emerged recently. One promising solution is to adopt the notion of statistical feasibility to account for the misspecification in dataset collection. Specifically, on top of chance-constrained programming, guaranteeing statistical feasibility modifies the original chance constraints by incorporating a confidence level requirement concerning dataset collection uncertainty. The modified chance constraints are dealt with from a robust optimization view by using purely data-driven methods, which improves statistically feasible robust optimization practicability. Compared with robust optimization, considering the confidence level grants decision-makers the flexibility in balancing cost-effectiveness and conservativeness. This chapter introduces statistical feasibility and provides its implementation to three typical power system operation tasks with different scales, including thermostatically controlled load scheduling, economic dispatch, and unit commitment, showcasing the effectiveness and scalability of robust operation with a statistical feasibility guarantee. Looking into the future, it is evident that the statistically feasible robust methods will contribute to sample efficient data-driven modern power system operation with high penetration of renewable energy.

3. Modeling and Operational Optimization of Hydropower Generation

   Yikui Liu, Lei Wu, Nan Yang

   Abstract

   Hydropower is recognized globally as a clean, high-quality, and flexible renewable energy source and is playing an increasingly crucial role in helping achieve carbon emission reduction and providing flexibility to power systems. Focusing on hydropower generation, this chapter discusses the development status and introduces the main types of hydraulic turbines and hydropower plants. The chapter focuses and discusses detailed modeling of the hydropower plants with reservoirs, run-of-the-river plants, cascaded hydropower systems, and pumped storage plants in the context of optimized operation.

4. Economic Dispatch of Wind and Thermal Bundled Power Systems

   Chao Lei, Qianggang Wang

   Abstract

   The chapter introduces a look-ahead rolling economic dispatch approach of wind and thermal bundled power systems. The chapter focuses on technical factors such as the variable ramp rate of retrofitted coal-fired units and flexible voltage-constrained load transfer strategy via high-voltage distribution networks. The chapter presents second-order cone ramping constraints for retrofitted coal-fired units, which can be validated with acceptable inner-approximated error, and employs for multiple wind and thermal bundled power system agents to accommodate wind power fluctuations for minimizing the production, purchasing, and switch-over operation costs. Moreover, the chapter introduces the multi-cut generalized Benders decomposition as a decentralized method to solve the sub-problems in parallel, which facilitates solving a large-scale rolling economic dispatch model. Through numerical case studies, the chapter shows the computing performance is satisfactory and has high efficiency.

5. Modeling of Natural Gas Dynamics Impact on Power Systems with High Renewables

   Reza Bayani, Saeed Manshadi

   Abstract

   Natural gas-fired generation units help mitigate the volatility of renewable energy sources. The fast-ramping capability of gas-fired units has rendered them as one of the best solutions to possible fluctuations in the system. This chapter focuses on the modeling of natural gas system dynamics and discusses that accurate modeling is essential to manage the volatile demand of these units, particularly during short-term fluctuations. While steady-state models are commonly used to simplify these dynamics, they are argued to be unreliable for short-term operations. The chapter first discusses different approaches to modeling natural gas dynamics and present several convex relaxation methods. It also introduces techniques for the coordinated operation of interconnected electricity-gas systems. Through the case studies, the chapter presents the steady-state model of natural gas optimization and highlights its drawbacks. Finally, the chapter shows that the rank-minimization relaxation technique can accurately and efficiently solve the non-convex natural gas flow equations.

6. Short-Term Prediction of Photovoltaic Power Generation

   Shahab Karamdel, Xiaodong Liang, Sherif O. Faried

   Abstract

   Solar photovoltaic (PV) is a dominant form of renewable energy source and has been widely deployed, but increasing PV power integrated into electric power grids creates challenges in the planning and operation of power grids due to PV power’s intermittency and variability nature. Accurate solar PV power generation prediction techniques are essential to overcome these challenges. This chapter proposes a short-term PV power generation prediction framework by using nineteen regression models within five regression families. To further enhance the forecasting model’s performance, hyperparameter optimization and tuning is discussed through the MATLAB Regression Learner toolbox. The historical datasets are utilized to develop the proposed forecasting models.

7. Optimal Placement of Photovoltaic Systems in Multi-Energy Systems

   Shahab Karamdel, Xiaodong Liang, Sherif O. Faried

   Abstract

   Solar photovoltaic (PV) systems can provide reactive power support through their interfacing smart inverters. This reactive power exchange can provide effective voltage regulation at the inverter’s connection point and supplement the voltage regulation already offered by legacy voltage regulation devices, on-load tap changers and capacitor banks. Conservation voltage reduction (CVR), the intentional operation of the electrical system to provide customer voltages in the lower end of the acceptable range to reduce energy and demand, has recently gained significant research interest due to voltage regulation support by the increasing integration of PVs. This chapter discusses that CVR benefits can be significantly enhanced by considering the location, inverter capacity and number of PV systems, and presents an optimal PV placement method to enhance CVR benefits in integrated electricity and natural gas systems, where the electrical and natural gas networks are linked via gas-fired distributed generations and power-to-gas units. The chapter then introduces an optimal PV placement problem to minimize load consumptions and the network power losses while observing all operational constraints in the system. The location of PVs and capacity of PV inverters are decision variables. Through case studies using the modified IEEE 33-bus electricity and 7-node natural gas test system, the chapter demonstrates that optimally placed PVs lead to significant improvement in load consumption reductions compared to randomly placed PVs, and a higher number of PVs provides higher power loss reductions for CVR implementation.

8. Modeling of Reactive Power-Voltage Response Characteristics of Renewable Energy Resources

   Chenge Gao, Ye Guo

   Abstract

   This chapter has focused on and discusses the challenge of modeling reactive power-voltage response characteristics of renewable energy sources. A physics-informed deep learning approach is employed in this chapter to incorporate the influence of the grid model into the training process. Moreover, to improve the adaptivity of the approach to time-varying operational and weather conditions and reduce the reliance on sample volume, a knowledge graph-based approach is used to store, search, and utilize pre-trained deep learning models. The chapter presents the simulation studies on a modified IEEE 39-bus system and a 2486-bus real system and illustrates the efficacy of the introduced method.

9. Carbon-Aware Distribution Network Operation and Optimization

   Linwei Sang, Yinliang Xu

   Abstract

   With the extensive integration of distributed resources into the distribution network, the operational management of the distribution system becomes pivotal to ensure its reliability, safety, and efficient operation. The interplay between the distribution network and distributed resources introduces complexity, manifesting in the intricate response of distributed resources to system incentive, thereby challenging their unified management. Leveraging machine learning and optimization theories, this chapter firstly introduces a constraint learning approach tailored to clusters of distributed resources. This involves establishing a data-driven response constraint model based on neural networks. Subsequently, the chapter discusses the fusing learning and optimization for distribution network operation framework through the lens of constraint learning. Based on the constraint model, carbon emission limitations, distribution network operational model, and bilinear relaxation strategies, an amalgamated machine learning and optimization-based distribution network operational model is constructed. This model not only can ensure the secure and economical operation of the distribution system but also addresses carbon emission management. Finally, through comprehensive case studies, the precision of constraint learning for distributed resource assimilation and the effectiveness of the integrated machine learning-optimization distribution network operational model are demonstrated in this chapter.

10. Distribution Network Reconfiguration

    Chao Lei, Siqi Bu

    Abstract

    Network reconfiguration is a classical optimal operation problem of electrical distribution networks. This problem aims at a specific topology optimization and chooses the optimal switch status of sectionalizing switches, tie-switches, and soft open points. The objective is maintaining real-time load balancing and loss reduction for distribution networks with the growing penetration of distributed energy resources and coordinating real-time transactive dispatch tasks between supply and demand at the market level of distribution networks. This chapter revisits the fundamentals of DistFlow equations and its convex relaxation formulations, and then introduces the disjunctive convex hull relaxation (DCHR) approach to tackle the disjunctive nature for distribution network reconfiguration problems. With continuous parent–child relationship variables as disjunctive variables, this DCHR approach is theoretically tighter than the McCormick linearization method and the Big-M method, and it is especially suitable for distribution networks with directional power flows. Through case studies, the chapter demonstrates the computing performance in terms of running time and iterations using a DCHR approach yields superior numerical performance than prior relaxation methods.