Review of trends and targets of complex systems for power system optimization

Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107

An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation Modes

In the last decades, new types of generation technologies have emerged and have been gradually integrated into the existing power systems, moving their classical architectures to distributed systems. Despite the positive features associated to this paradigm, new problems arise such as coordination and uncertainty. In this framework, microgrids constitute an effective solution to deal with the coordination and operation of these distributed energy resources. This paper proposes a Genetic Algorithm (GA) to address the combined problem of Unit Commitment (UC) and Economic Dispatch (ED). With this end, a model of a microgrid is introduced together with all the control variables and physical constraints. To optimally operate the microgrid, three operation modes are introduced. The first two attend to optimize economical and environmental factors, while the last operation mode considers the errors induced by the uncertainties in the demand forecasting. Therefore, it achieves a robust design that guarantees the power supply for different confidence levels. Finally, the algorithm was applied to an example scenario to illustrate its performance. The achieved simulation results demonstrate the validity of the proposed approach.Ministerio de Ciencia, Innovación y Universidades TEC2016-80242-PMinisterio de Economía y Competitividad PCIN-2015-043Universidad de Sevilla Programa propio de I+D+

Compressed Air Energy Storage-Part II: Application to Power System Unit Commitment

Unit commitment (UC) is one of the most important power system operation problems. To integrate higher penetration of wind power into power systems, more compressed air energy storage (CAES) plants are being built. Existing cavern models for the CAES used in power system optimization problems are not accurate, which may lead to infeasible solutions, e.g., the air pressure in the cavern is outside its operating range. In this regard, an accurate CAES model is proposed for the UC problem based on the accurate bi-linear cavern model proposed in the first paper of this two-part series. The minimum switch time between the charging and discharging processes of CAES is considered. The whole model, i.e., the UC model with an accurate CAES model, is a large-scale mixed integer bi-linear programming problem. To reduce the complexity of the whole model, three strategies are proposed to reduce the number of bi-linear terms without sacrificing accuracy. McCormick relaxation and piecewise linearization are then used to linearize the whole model. To decrease the solution time, a method to obtain an initial solution of the linearized model is proposed. A modified RTS-79 system is used to verify the effectiveness of the whole model and the solution methodology.Comment: 8 page

Generic Market Modelling for Future Grid Scenario Analysis

Power systems worldwide are moving away from being dominated by large-scale synchronous generation and passive consumers. Instead, in the future, new actors on both the generation and the load side will play an increasingly significant role. On the generation side, there are renewable energy resources (RES) such as wind generation (WG), photovoltaic (PV) and concentrated solar thermal (CST). On the load side, there are demand response (DR), energy storage and price responsive users equipped with a small-scale PV-battery system (called prosumers). The two sides will together shape future grids. However, if connected at a large scale without proper consideration of their effect, they can also jeopardise the reliability and security of electricity supply. For example, the addition of non-synchronous RES will jeopardise the frequency response of the future grids, while the intermittency and variability of RES threats the existing model of electricity supply (supply following demand), complicating balancing and stressing future grids’ ramping capabilities. On the other hand, the inclusion of DR, prosumers and storage without proper consideration of the implications can cause significant changes to the demand profiles and may result in new stresses such as secondary peaks or excessive ramps. In summary, balancing, stability (frequency, voltage, transient) and ultimately reliability are affected by the changes introduced to the future grids’ technology mix. Given that the lifespan of power system assets is well over fifty years, laying out a roadmap to future grid development in an economical fashion without risking its security is a challenging task. The uncertainty of cost, availability and quality of new technologies requires power system planners and policy-makers to evaluate the feasibility and viability of future grids for a diverse range of technology options. To this end, a rigorous and systematic approach is developed in this dissertation to analyse the implications of prosumers, storage and CST on the balancing and stability of future grids. The best features of all these approaches are combined and presented in a single coherent framework. Computation time improvement techniques are then deployed to improve the computational efficiency and solution accuracy. Taken as a whole, the tool will fill the gap to explore the validity of emerging technologies to tackle balancing, stability, security and reliability issues, over a diverse scope of uncertain premises. The tool is developed for an approach to future grids studies called scenario analysis. Traditionally, power systems are planned based on a handful of the most critical scenarios with an aim to find an optimal generation and/or transmission plan. In contradistinction, scenario analysis involves analysing possible evolutionary pathways to facilitate informed decision making by policy-makers and system planners. Specifically, the primary aim of future grids studies is to deal with the uncertainty of long-term decision making and providing outcomes that are technically possible, although explicit costing might be considered. To this end, for any future grids stability framework, the market model is a critical bottleneck. Existing future grids studies mostly look at simple balancing, ignore network constraints and include most of the emerging technologies in an ad hoc fashion. These simplifications are made to combat the high computation time requirement of accurate approaches. Against this backdrop, this dissertation presents: i) a novel optimisation-based models to capture the effects of prosumers (Chapter 2, 3); ii) co-optimise dispatch of PV and CST aggregation to reduce ramping stress on the conventional generators (Chapter 4); iii) efficiently implemented market-based dispatch (Chapter 5); iv) framework for frequency performance assessment of future grids (Chapter 6). In more detail, first, Chapter 2 and 3 develop a novel approach to explicitly model prosumers’ demand in market dispatch (production cost) models. The key novelty of the method is its ability to capture the impact of prosumers without going into specific market structure or control mechanisms, which are computationally expensive. The model is formulated as a bi-level program in which the upper-level unit commitment (UC) problem minimises the total generation cost and the lower-level problem maximises prosumers’ aggregate self-consumption. Unlike the existing bi-level optimisation frameworks that focus on the interaction between the wholesale market and an aggregator, the coupling is through the prosumers’ demand, not through the electricity price. That renders the proposed model market structure agnostic, making it suitable for future grids studies where the market structure is potentially unknown. This model addresses some critical questions such as, How much flexibility can prosumer provide to help with large-scale RES integration? Flexibility is the key to achieve a high RES penetration. One of the major problem in the integration of RES is their intermittent and variable nature. Concentrated solar thermal (CST) presents an excellent resource with inherent flexibility. In contrast to Chapter 2 and 3 (exploring flexibility through DSM), Chapter 4 examines flexibility options from a generation end. In particular, it proposes an RES aggregation (REA) scheme aiming to co-optimise the dispatch of intermittent and dispatchable RES. The principal aim is to keep in check the ramping stress imposed on the conventional generators due to the RES integration. A Stackelberg game is used to capture the interaction between an independent system operator (ISO) and the REA when the ISO tries to minimise the generation cost, while REA seeks to maximise its revenue. This approach also highlights the potential of a ramping market, as proposed by some US studies. In Chapter 5, the utility storage proposed in Chapter 2, prosumers model proposed in Chapter 3, the dispatch model of CST developed in Chapter 4 and inertia constraint detailed in Chapter 6 are combined into a single coherent framework. The addition of these emerging technologies in the energy market model significantly increases the computation burden. Also, to allow for a subsequent stability assessment, an accurate representation of the number of online generation units is required, which affects the power system inertia and the reactive power support capability. This renders a fully-fledged market model computationally intractable, so in Chapter 5 we deploy unit clustering, a rolling-horizon optimisation approach and constraint clipping to improve the computational efficiency. Together, these comprise a computationally efficient market simulation tool (MST) suitable for future grid stability analysis. Finally, developed MST is used in Chapter 6 for a comprehensive frequency performance assessment of the Australian National Electricity Market (NEM). First, an assessment of minimum inertia requirements is presented, followed by a framework for frequency performance assessment of future grids. The maximum non-synchronous instantaneous range from a frequency performance point of view is established for the NEM. Also, to alleviate the deteriorating effects of the high RES penetration on frequency performance, different technical solutions are proposed and discussed. These efforts will empower policy-makers and system planners with the information on safe penetration levels of different technologies while ensuring reliability and security of future grids

Increasing thermal plant flexibility in a high renewables power system

Thermal generation is a vital component of mature and reliable electricity markets. As the share of renewable electricity in such markets grows, so too do the challenges associated with its variability. Proposed solutions to these challenges typically focus on alternatives to primary generation, such as energy storage, demand side management, or increased interconnection. Less attention is given to the demands placed on conventional thermal generation or its potential for increased flexibility. However, for the foreseeable future, conventional plants will have to operate alongside new renewables and have an essential role in accommodating increasing supply-side variability. This paper explores the role that conventional generation has to play in managing variability through the sub-system case study of Northern Ireland, identifying the significance of specific plant characteristics for reliable system operation. Particular attention is given to the challenges of wind ramping and the need to avoid excessive wind curtailment. Potential for conflict is identified with the role for conventional plant in addressing these two challenges. Market specific strategies for using the existing fleet of generation to reduce the impact of renewable resource variability are proposed, and wider lessons from the approach taken are identified