Power System Flexibility

This introductory chapter serves to motivate the subject matter of this book by providing a brief overview of the power system flexibility challenges caused by the increasing penetration of renewable energy as well as the corresponding technologies and solutions to combat the ambitious carbon neutral targets. Specifically, we first introduce the history and development of the power system flexibility and propose a taxonomy for power system flexibility resources in terms of supply-side flexibility, grid flexibility, demand-side flexibility and flexibility from energy storage. Then, we provide a short overview of the framework of power system flexibility analysis from the perspective of power supply chain. In the end, we briefly introduce how to solve the problem of power system flexibility by effective market mechanism design.

The power system flexibility refers to the adaptability of the system to internal and external uncertain factors, that is, the response capability of the system when internal or external variables change. Flexible resources come from all regulation means that can deal with the system volatility and uncertainty. Flexible resources in power systems can be defined as a set of resources in which the “power source-network-load” link can provide a certain regulation capability to adapt to the random changes of power systems (such as renewable energy output uncertainty, load fluctuation, and DC lock caused by grid failure) under a given time scale. The role of flexible resources is to act as a “flexible power regulation” to provide sufficient margin to meet the flexibility requirements of the system. This chapter divides flexible resources according to the three dimensions of “power source-network-load”, and introduces the technical and economic characteristics of flexible resources in the multiple links of “power source-network-load”.

Reasonable scheduling flexible resources play an important role in the reliable operation of high proportion renewables energy power systems. However, how to determine the system operation decision respecting the system security constraints and flexibility demand is still an open problem. In this chapter, the concept of wind power accommodation domain (ADWP) is proposed in Sect. 3.2 as a metric for quantifying flexibility demand and an operational guideline, and a flexible look-ahead unit commitment (LAUC) model is established. In Sect. 3.3, a two-level decision model of day-ahead generation-reserve considering flexibility demand is proposed, and the quantitative relationship between operation reserve and flexibility is established through the general generation function method. In Sect. 3.4, an improved endogenous reserve determination method is proposed. A variety of uncertainties are integrated into the security and network constrained economic scheduling model and solved iteratively with the help of Benders Decomposition. In Sect. 3.5, IEEE-30 bus, and IEEE-118 bus systems are taken as case studies to verify the effectiveness of proposed models.

The increasing integration of volatile renewables energy (VRE) has highlighted the importance of flexibility in power system operation. Due to the imbalance in development between large-scale VRE and electricity load centers, the transmission of large amounts of VRE has drawn substantial attention. Given that HVDC offers a unique capability in terms of the regulation of power flow, the hybrid AC/DC grid can operate more flexibly and cost-effectively. Considering computing efficiency, autonomy of each regional grid and information privacy, compared with centralized dispatch approach, distributed dispatch approach is more suitable. So, we propose a distributed, hierarchical and robust dispatch approach in AC/DC hybrid systems with high flexibility requirement. Multi-stage multi-area economic dispatch models are formulated with hybrid AC/DC links and VSC-MTDC networks respectively and are solved distributedly. Case studies based on hybrid AC/DC systems of 3-area 18-bus, 3-area 117-bus, 3-area 354-bus, 4-terminal with four 6-bus systems and 4-terminal with four 118-bus systems demonstrate the effectiveness and efficiency of the proposed mechanism and method.

With the ever-increasing integration of renewable energy sources represented by wind and solar energy into power grids, its strong uncertainty and fluctuation present great challenges to the flexible operation of power grids. The traditional power regulation means of “generation-side” and “demand-side” are limited. With the rapid development of power electronics technology and other emerging control technology, the voltage source converter-based high voltage direct current transmission (VSC-HVDC) and the transmission switch (TS) provide new power regulation means on the “grid-side”. In this chapter, the operational flexibility exploration of AC/DC power gird is researched. We try to describe the flexible operation mechanism of the power grid and propose the flexible operation improvement means. The case studies verify the proposed model.

With the continuous development of the types, functions, and controllability, demand-side resources begin to respond actively according to the operation state of power systems. The power demand side contains abundant adjustable resources, and have great potential in improving the flexibility of power systems. This chapter investigates the demand side flexibility from following three aspects: residential load side demand response, price incentive demand-side response, and integrated energy system demand-side response. In load-side demand response, a complete scheduling scheme is modeled based on the optimization of residential loads and distributed generation. The presented model reduces the cost of the user’s electricity consumption and decreases the peak load and peak-valley difference of smart residential load without bringing discomfort to the users. As for price incentive demand-side response, an energy management model is established for intelligent temperature control appliances of commercial and residential customers which can optimize its electricity consumption according to power prices under the smart grid. And in the integer energy system side, a resilience-oriented stochastic integrated energy system configuration framework considering integrated demand response influence is proposed to improve the flexibility and resilience of the power system.

This chapter studies the aggregation of large-scale distributed flexibility resources, and aggregates a large number of flexible loads into a small number of aggregation load models. This chapter mainly considers large scale demand response aggregation and distributed energy storage aggregation. From the study of large-scale demand response and distributed energy storage, it is found that they need effective clustering and aggregation equivalence methods to give play to their flexibility value. Demand response contains a large number of transferable and interruptible loads, when large-scale interruptible and shiftable load is connected to the power system and its operation mode is optimized, the system operation cost will be reduced and greater flexibility will be provided for the system. The most representative of distributed energy storage is electric vehicle (EV), the randomness of large-scale electric vehicles will significantly influence the reliable and economic operation of power systems, while integrating a large population of EVs into the system-level operation and market bidding makes sense, so it is essential to construct an aggregate model to capture their available flexibility.

This chapter takes balancing market mechanism design as an example, to studies the possible impact of balancing market mechanism design on improving power system flexibility. With the increasing proportion of renewable energy in the power system, it is necessary to improve it from the aspect of power market. Due to its short-term trading characteristics, power spot market (balancing market) has become a key link in designing market mechanism to improve flexibility. Taking the participation of wind power suppliers in the trading process of power spot market as an example, due to its inherent uncertainty and volatility, renewable energy such as wind power intensifies the real-time power imbalance in the market, which makes it more difficult to balance the operation of market links and related unbalancing settlement in the spot market. Considering the market environment, it is necessary to analyze and balance the market operation effect through effective methods, and simulate and evaluate the interaction between the behaviors of market members. Firstly, this chapter introduces the framework of balancing market and summarizes the key elements of unbalancing settlement in it, including assessment tolerance margin, programme time unit (PTU) duration and unbalancing power settlement price mechanism. Then two analysis models are proposed. The first is the optimization model for wind power suppliers who can offer strategically to participate in spot market transactions. The second model is based on the ABM method with multi criteria decision analysis (MCDA). The two models seek the optimal design with the minimum total unbalancing cost of the market from different perspectives, and then conduct numerical analysis based on the above models. Finally, the corresponding suggestions are provided for the design method of balancing market mechanism to improve the flexibility of power system.