Temporal–Spatial Acceleration Framework for Full-Year Operational Simulation of Power Systems with High Renewable Penetration

With the development of power systems under high renewable penetration, the concentrating solar power (CSP) plant, which can provide both renewable energy and operational flexibility, have attracted more and more attention. However, most existing CSP modeling and optimization methods have the following limitations: applying the mixed integer linear programming model to the annual operation cost analysis of the long-term planning problem for large-scale systems is complicated. In this paper, we introduce a fast cluster optimization method for CSP plants toward power systems under high renewable energy penetration. Through this method, the CSP plants with similar operation characteristics in adjacent geographical areas are clustered and grouped, so as to optimize the group behavior, rather than the behavior of individual plant. The number of decision variables is greatly reduced and the mixed integer linear programming model is transformed into a linear optimization model by introducing additional linear (continuous) variables instead of integer variables, thus significantly reducing the computational complication and realizing the rapid calculation of long-term planning for large-scale power systems. We also apply the proposed model to collaborative optimization of power system planning and operation for a province in China to prove the effectiveness of the proposed model and the solution method.

Generation scheduling and unit commitment procedures in a power system constitute a key operational planning feature of any power system. The complex, constrained unit commitment problem presents a computational challenge, and improving the solution quality can make a substantial impact in the long run. This paper presents an improved binary grey wolf optimization (IBGWO) to solve the unit commitment problem in power system operational planning. The IBGWO enhances the balance between exploration and exploitation properties of the grey wolf search. This would improve both the local as well as the global search properties of the classical grey wolf algorithm. In order to execute and implement the improved grey wolf optimization to unit commitment with binary decision variables, binary transformation of the real valued variant is employed. The IBGWO is tested using test system with different sizes ranging from 10 thermal units to 100 thermal units. The solution quality indices along with convergence characteristics are presented and compared to the existing approaches. The same demonstrates the improved solution quality in the form of reduced operational cost.

Benefits of transmission switching and energy storage in power systems with high renewable energy penetration

This paper develops a model for scheduling large-scale hydrothermal power systems based on the Mixed Integer linear Programming (MILP) technique. The advantage of this model is that the schedules can make coordinated decision for hydro and thermal units, take into full account the hydro unit constraints in achieving overall economy of power system operation. The planning problem is not decomposed into sub-problems for hydro and thermal units, instead hydro and thermal units are scheduled to find solution of global optimization. This paper concentrates on the solution methodology for hydro problem with electric quantity constraints and discrete hydro constraints, and coordinative optimization model of hydrothermal power systems on the basis of Security Constrained Unit Commitment (SCUC) and Security Constrained Economic Dispatch (SCED) are discussed in the paper. The model is illustrated by example of a provincial network power system. Numerical result based on this practical system show that this method is efficient and effective for dealing with hydrothermal systems.

Guest Editorial: Situational awareness of integrated energy systems

Frequency control using loads and generators capacity in power systems with a high penetration of renewables

Facing ever-growing fossil energy crises and climatic and environmental issues, China pledges to the international community to peaking its carbon emissions by 2030 and achieving carbon neutrality by 2060. Hence, building a new power system with high renewable penetration is inevitable to decarbonizing China’s energy sector and achieving its great targets of carbon peaking and neutrality. Nevertheless, high renewable penetration into the grid will fundamentally alter the characteristics of the power system, featuring burgeoning uncertainty, diversified system operation, and complex stability control. On the foundation of this prospect, from the perspectives of safe and reliable system operation, economic operation, and system planning, this paper teases out potential challenges raised by high renewable penetration into the new power system, and explores the key sci-tech issues arising from high renewable penetration, such as power forecast, and system stability control, as well as market mechanism design and spatial-temporal energy balancing, with a view to providing method thoughts and theory support for future research.

Chance-constrained unit commitment with energy storage systems in electric power systems

A game-theoretic model for wind farm planning problem: A bi-level stochastic optimization approach

<p style='text-indent:20px;'>In this paper, we consider a joint pricing and inventory problem with promotion constrains over a finite planning horizon for a single fast-moving consumer good under monopolistic environment. The decision on the inventory is realized through the decision on inventory replenishment, i.e., decision on the quantity to be ordered. The demand function takes into account all reference price mechanisms. The main difficulty in solving this problem is how to deal with the binary logical decision variables. It is shown that the problem is equivalent to a quadratic programming problem involving binary decision variables. This quadratic programming problem with binary decision variables can be expressed as a series of conventional quadratic programming problems, each of which can be easily solved. The global optimal solution can then be obtained readily from the global solutions of the conventional quadratic programming problems. This method works well when the planning horizon is short. For longer planning horizon, we propose a multi-stage method for finding a near-optimal solution. In numerical simulation, the accuracy and efficiency of this multi-stage method is compared with a genetic algorithm. The results obtained validate the applicability of the constructed model and the effectiveness of the approach proposed. They also provide several interesting and useful managerial insights.

Novel MILP and CP models for distributed hybrid flowshop scheduling problem with sequence-dependent setup times

This study presents a new approach for optimal placement of synchronised phasor measurement units (PMUs) to ensure complete power system observability in the presence of non‐synchronous conventional measurements and zero injections. Currently, financial or technical restrictions prohibit the deployment of PMUs on every bus, which in turn motivates their strategic placement across the power system. PMU allocation is optimised here based on measurement observability criteria for achieving solvability of the power system state estimation. Most of the previous work has proposed topological observability based methods for optimal PMU placement (OPP), which may not always ensure numerical observability required for successful execution of state estimation. The proposed OPP method finds out the minimum number and the optimal locations of PMUs required to make the power system numerically observable. The problem is formulated as a binary semi‐definite programming (BSDP) model, with binary decision variables, minimising a linear objective function subject to linear matrix inequality observability constraints. The BSDP problem is solved using an outer approximation scheme based on binary integer linear programming. The developed method is conducted on IEEE standard test systems. A large‐scale system with 3120 buses is also analysed to exhibit the applicability of proposed model to practical power system cases.

Day-ahead reserve determination in power systems with high renewable penetration

Intentional islanding operation (IIO) is a feasible solution to improve the reliability of active distribution network (ADN) by supplying critical loads through the local DG when a fault occurs. Aiming at this goal, a new two‐stage methodology is proposed to supply critical loads based on cost‐effective improvement. In the first stage, the interruption cost is proposed as the load priority and the ON/OFF status of switches are considered as the binary decision variables. Therefore, IIO is considered as a mixed integer linear programming (MILP) problem to minimise the interruption cost. At the second stage, the power flow calculation is performed on the initial islands for the real‐time operation. The proposed method can be utilised for both long‐ and short‐term studies. In the long‐term study, the inherent uncertainty of ADN is considered in MILP by using a Monte‐Carlo simulation. This concept is used for clustering ADN into self‐sufficient microgrids. Moreover, by taking a snapshot of the ADN status and performing operational feasibility, the proposed method can be considered as a real‐time power regulation method. The proposed methodology is implemented on the IEEE 33‐bus distribution network, and the results are discussed in detail.

Inertia estimation of power system with new energy considering with high renewable penetrations