Optimal Operation Method Research Articles

Frequency Control and Optimal Operation of Low-Inertia Power Systems With HVDC and Renewable Energy: A Review

With the integration of large-scale renewable energy sources (RES) and high voltage direct current (HVDC) based inter-regional transmission, modern power systems are facing low-inertia issues and poor frequency regulation capabilities. This paper conducts a comprehensive review on frequency control and optimal operation methods in this context. First, the frequency characteristic of a low-inertia power system is analyzed covering the whole frequency response process. Secondly, the impacts of RES and HVDC integration on system inertia and frequency performance are studied, and the system frequency characteristics considering RES and HVDC are analyzed. Then, the frequency response modes of various frequency regulation resources are compared from the perspective of “source-grid-load-storage”, including their basic control principles and frequency control strategies. Further, from the operation dispatch perspective, the frequency security-constrained power system optimal operation methods are reviewed. Finally, future research trends in this area are discussed.

A Decentralized Bi-Level Decomposition Method for Optimal Operation of Electric Vehicles in Coupled Urban Transportation and Power Distribution Systems

A Decentralized Bi-Level Decomposition Method for Optimal Operation of Electric Vehicles in Coupled Urban Transportation and Power Distribution Systems

A Novel Dynamic Operation Optimization Method Based on Multiobjective Deep Reinforcement Learning for Steelmaking Process.

This article studies a dynamic operation optimization problem for a steelmaking process. The problem is defined to determine optimal operation parameters that bring smelting process indices close to their desired values. The operation optimization technologies have been applied successfully for endpoint steelmaking, but it is still challenging for the dynamic smelting process because of the high temperature and complex physical and chemical reactions. A framework of deep deterministic policy gradient is applied to solve the dynamic operation optimization problem in the steelmaking process. Then, an energy-informed restricted Boltzmann machine method with physical interpretability is developed to construct the actor and critic networks in reinforcement learning (RL) for dynamic decision-making operations. It can provide a posterior probability for each action to guide training in each state. Furthermore, in terms of the design of neural network (NN) architecture, a multiobjective evolutionary algorithm is used to optimize the model hyperparameters, and a knee solution strategy is designed to balance the model accuracy and complexity of neural networks. Experiments are conducted on real data from a steelmaking production process to verify the practicability of the developed model. The experimental results show the advantages and effectiveness of the proposed method compared with other methods. It can meet the requirements of the specified quality of molten steel.

Multi-time scale dynamic operation optimization method for industrial park electricity-heat-gas integrated energy system considering demand elasticity

This study proposes a multi-time scale dynamic optimization (MSDO) method for ultra-short-term scheduling in industrial electricity-heat-gas integrated energy systems. The aim is to enhance the feasibility of scheduling schemes and to utilize flexibility potential. The MSDO method considers equipment response differences and demand fulfillment time disparities. The dynamic aspect is reflected in the supply-demand matching within the scheduling time window. A single-layer model with unified temporal granularity is constructed based on lag parameter identification. The study proposes various forms of energy demand elasticity, including steam networks and cumulative demand, and investigates the optimal minimum temporal granularity for scheduling. Case results demonstrate that the MSDO method effectively characterizes the multi-energy time lag of equipment. Compared with methods disregarding lag parameters, the MSDO method enhances total profit by ¥ 120711.42 and improves operation stability by 2.96%. The MSDO method outperforms baseline methods in cost and stability, accommodating a 160% increase in renewable energy growth. Under different elasticity spaces, the profit of the cumulative demand method exceeds that of the average method. The time lag of different equipment impacts scheduling profits. An optimal choice exists for the minimum temporal granularity to strike a balance between computational efficiency, economy, and feasibility.

Optimal Hybrid Pulse Width Modulation for Three-Phase Inverters in Electric Propulsion Ships

Global interest in environmentally friendly ships has surged as a result of greenhouse gas reduction policies and the demand for carbon neutrality. Despite growing demand for electric propulsion systems, there is a lack of research and development on crucial components. Efficiency and stability are primarily influenced by the performance of inverters, which are essential for driving propulsion motors. Existing inverter control techniques can be of two types: continuous-PWM (pulse width modulation) methods for harmonic performance enhancement and discontinuous-PWM methods for efficiency improvement by reducing losses. However, there are limitations in that each PWM method exhibits substantial variations in inverter performance based on its operating conditions. To address these challenges, this study proposes the hybrid pulse-width-modulation (HPWM) method for optimal inverter operation. By analyzing the inverter’s operating conditions, the proposed HPWM method adopts various pulse-width-modulation (PWM) strategies based on a modulation index to achieve harmonic improvement and loss reduction. Our focus is on comparing and analyzing diverse PWM techniques under varying modulation indices and frequency conditions to attain the optimal operating conditions. Experimental validation of the proposed method was conducted using a 2.2 kW dynamometer. In comparison with existing PWM methods, the proposed method demonstrated superior performance.

Multi-time scale operation optimization for a near-zero energy community energy system combined with electricity-heat-hydrogen storage

Hybrid energy storage offers a new way to increase the renewable energy proportion (REP) of near-zero energy community energy system (NZECES). However, the optimization operation method of NZECES combining hybrid energy storage is not yet mature. Therefore, this paper proposes integrating electricity storage, heat storage and hydrogen storage into the NZECES, and conducting multi-time scale (MTS) operation optimization research. Firstly, a NZECES with electricity-heat-hydrogen storage is constructed. Secondly, a MTS optimization operation model of day-ahead scheduling, intra-day rolling, and real-time adjustment is established. Among them, the hourly scheduling scheme targeting the lowest daily operating cost is obtained through the optimization of the day-ahead stage. In the intra-day stage, the scheduling scheme with a time interval of 15 min is obtained by the rolling optimization. The operation scheme of electrical-related equipment with the minimum adjustment cost is obtained in the real-time stage. Finally, a NZEC with different building types is used as the energy supply object. The results show that compared to the traditional day-ahead scheduling scheme, the REP is increased by 3.6 % and the operation cost of the energy system is reduced by 5.3 % by using the MTS operation optimization method.

Empirical study on optimization methods of building energy operation for the sustainability of buildings with integrated renewable energy

Owing to the challenges caused by climate change, the spread of green buildings is increasing. Moreover, it is becoming necessary to secure sustainability through the management of new and renewable energy operations. Accordingly, this paper describes the proposal and empirical demonstration of an energy operation optimization method for renewable energy-integrated buildings utilizing photovoltaics (PVs) and energy storage systems (ESSs). The proposed optimization method begins with the calculation of a weather index (WI) based on environmental and climatic data, with particular emphasis on its relevance to PV production. Additionally, we develop the weight-prox algorithm (W-PA) for controlling and optimizing daily building energy operations. The goal of this optimization method is to enhance the performance of three sustainability indicators of green buildings. Simulations are then conducted in a digital transformation environment using real green buildings as targets. The results demonstrate the improved performance of sustainability indicators, including renewable energy efficiency, economic resilience, and eco-friendliness. The output of this study can serve as a set of guidelines that considers the convenience of implementation for building operators. This study is also significant by raising awareness of the need for climate change response and contributing to the use of renewable energy for the sustainability of green buildings.

Low-carbon operation optimization of integrated energy system considering CCS-P2G and multi-market interaction

Integrated energy system is crucial in realizing China’s “dual carbon” targets. Considering the carbon capture based electricity to gas and the interaction of multiple markets, this paper proposes a low-carbon operation optimization method of integrated energy system. In terms of market policy, a coupling trading mechanism for carbon trade and green certificates is established. This approach is intended to delve into the profound significance of utilizing green certificates in carbon emission reduction. In terms of equipment models, the coupling model of carbon capture equipment with coal-fired cogeneration unit, as well as power-to-gas equipment with renewable energy, is con-structed. In addition, this equipment model is introduced into the operation optimization scheduling of the comprehensive energy systems. A low-carbon economic operational strategy is further proposed to minimize the daily operational costs, by which the integrated energy system is eco-nomically, environmental protection optimized. To verify the effectiveness and feasibility of the proposed model, this paper sets up several comparison scenarios and conducts the simulations using GUROBI solver. The results show that the proposed strategy can effectively improve the uptake rate of renewable energy, reduce the carbon emission, improve the operation economy, and realize the complementary incentive effect between markets.

A Foresight-Seeing and Transferable Optimization Method for Synergic Operation of Multiple Flexible Resources in Active Distribution Network

A Foresight-Seeing and Transferable Optimization Method for Synergic Operation of Multiple Flexible Resources in Active Distribution Network

Operation optimization method of integrated energy system in park considering carbon transaction cost

Considering the demand of energy consumption and carbon reduction in industrial parks, combined with the coupling relationship of different energy conversion and the characteristics of equipment operation, the typical equipment in the park is modeled. Based on the carbon emission allocation, the carbon transaction cost is quantified, and the operation optimization method of the integrated energy system(IES) for park is proposed with goal of economy and low carbon. Taking an industrial park as an example, the results of the two scenarios of subsystem discrete operation and integrated energy system optimal operation are analyzed. The results show that the construction of IES in park found on carbon trading and the optimization method have a significant effect on the realization of energy saving and carbon reduction and cost reduction.

Research on the Operation Optimization of Aggregate Resources under the Dual Factors of Power Grid and Economy

Under the influence of power grid and economy, the operation optimization of aggregate resources has high practical value and significance. For the problems of the existing aggregate resource operation optimization methods, this paper presents a completely new optimization method, considering the economic factors of power output, grid line transmission, grid stability, transformer capacity, energy storage charging and discharge loss, grid transmission loss, transformer loss and production arrangement of adjustable resource aggregate, and combined with the idea of electric power translation in the operation of the load device. By adopting a mixed-integer linear programming model (Mixed-Integer Linear Programming, MILP), the operation optimization problem of the aggregate resource is transformed into a mathematical planning problem to be solved, which effectively avoids the start-stop delay of the load device, and shortens the time taken to adjust power supply and demand, reduces the construction cost and operation and maintenance cost of the aggregate, and realizes the optimal allocation of electric power resources, improves the sustainable energy utilization rate and the stability of the power system, and provides a new direction for the continuous development and progress of the power system.

Modeling and optimal operation of reversible solid oxide cells considering heat recovery and mode switching dynamics in microgrids

The reversible solid oxide cell (rSOC) is a promising technology for advancing energy decarbonization by enabling bidirectional conversion between electricity and hydrogen in a single device. However, previous studies have not fully explored the operational flexibility of rSOCs due to inadequate consideration of heat recovery potentials and dynamics of operating mode transitions. To address this research gap, this paper presents a model-based optimal operation method for managing multi-energy transactions in rSOC-based microgrids, aiming to minimize operation costs. The method incorporates detailed operational models of the rSOC, including a lumped thermal model to account for heat recovery capability and modeling of various operating modes and their transitions. Additionally, a linearization process is introduced to address nonlinear and implicit operational constraints, resulting in a computationally efficient mixed-integer linear programming (MILP) formulation for the operation model. Comparative case studies are conducted using modified energy portfolios of a Danish energy island. The results demonstrate that the proposed method effectively captures operating mode transitions within the rSOC and enhances its profitability via waste heat recovery. Notably, the rSOC model contributes to enhanced operational flexibility through heat recovery behaviors and a wider temperature range, resulting in substantial economic savings for the microgrid.

Optimal operation of coordinated multi-carrier energy hubs for integrated electricity and gas networks

"In the field of energy supply, coordinated multi-carrier systems, which include natural gas and electric energy, offer unique opportunities to enhance energy efficiency and flexibility. Nevertheless, the interdependence between electricity and natural gas networks poses various challenges related to the flow of electricity and gas in feeders, pipes, and connection points. To address these challenges, the concept of an energy hub emerges as an essential component in multi-carrier energy systems, serving as a connection point between the electricity grid and gas grids. This study introduces an optimal operation method for coordinating gas and electricity distribution networks through interconnected energy hubs. The infrastructure of the proposed energy hub comprises a combined heat and power unit, a boiler, an electric energy storage system, a heat pump, and a gas-burning unit, effectively meeting heating and electrical load requirements. The model formulation follows a stochastic approach based on a two-stage scenario. Considering uncertainties related to wind energy, electric load, and real-time energy prices, the primary objective of this study is to minimize the total operating cost of the energy system. The optimization problem is solved using Wall's optimization algorithm. To meet its energy requirements, the integrated energy system presented in this paper actively participates in the real and daily electric energy markets, as well as the natural gas market, providing the necessary energy resources. Additionally, the model incorporates realistic descriptions of electric power and gas flow in feeders and gas pipelines, considering AC load distribution and the Weymouth equation. With consideration of connectivity constraints, the proposed model offers a comprehensive representation of the integrated electricity and gas networks. The simulations conducted on the integrated energy system, comprising a 33-bus electric network and a 6-node gas network with interconnected energy hubs, highlight a remarkable 12 % decrease in electricity purchase costs and a substantial 18 % reduction in natural gas procurement expenses, underscoring the cost-efficiency and effectiveness of the stochastic planning approach.

Multi-objective Collaborative Optimization Method for Intelligent Distribution Network Operation and Maintenance

Most of the conventional multi-objective coordination methods for active distribution network operation are designed based on the principle of weighted random algorithm, which has limited application scope and cannot simplify the algorithm and reduce the complexity of the solution process, resulting in poor stability of active distribution network lines and potential faults. Based on this, this algorithm is introduced to carry out multi-objective collaborative optimization for intelligent distribution network operation. Firstly, the node types for the intelligent distribution network are defined and the power flow of the distribution network is analyzed. Secondly, a multi-objective configuration model for the lower level of the operation is constructed to maximize the distribution network investment and operation benefit index. A multi-objective collaborative optimization algorithm based on an enhanced particle swarm optimization algorithm is designed to reduce the operation cost of the distribution network and enhance the security and stability of the distribution network system during operation. The simulation test results show that after the application of the new method, the intelligent distribution network voltage stability index Vm is always less than 1, and the line stability has been significantly improved.

A Stackelberg Game Based Optimization Method for Heterogeneous Building Aggregations in Local Energy Markets

This paper proposes an optimal operational method and a customized pricing strategy for a Microgrid (MG) incorporating heterogeneous buildings from different communities in a local energy market (LEM). A customized pricing strategy in the LEM for the heterogeneous building aggregations (BAs) is proposed considering their heterogeneous insulation performances. In this regard, a hierarchical framework with two levels for optimal decision-making of the MG as the price maker and BAs as the price takers in the LEM are presented. At the upper level, the MG aims to maximize its profit by optimizing the customized prices and its energy schedules. At the lower level, a detailed physical model of the building with adjustable heating, ventilation, and air conditioning (HVAC) systems is developed while considering the building's thermal dynamics. The objective of each BA in each community is to minimize its operating cost while ensuring the users' thermal comfort according to MG's prices. The Stackelberg game is used to model the interactions between the MG (leader) as the price maker and multiple heterogeneous BAs (followers) as the price takers in the LEM. Then, the proposed optimization model is converted into a mixed integer linear programming using the Karush-Kuhn-Tucker conditions and strong duality theorem. Numerical results show that the hierarchical method benefits both the MG and heterogeneous BAs with customized pricing schemes. The HVAC of the building with good thermal insulation performance can make more adjustments to indoor temperature to reduce its operating costs. Moreover, it has a more significant impact on MG's electricity prices.

Optimal Operation Method for Electricity–Heat Integrated Energy System Considering Vulnerability Prevention

Optimal Operation Method for Electricity–Heat Integrated Energy System Considering Vulnerability Prevention

Risk-managed operation of community integrated energy systems in day-ahead and real-time markets based on portfolio theory

Operation strategy is a critical issue for the community integrated energy system (CIES) to improve its economic performance. However, the uncertainties in energy markets may result in severe deviation and fluctuation in the operation cost. It is challenging to balance the cost and risk in the operation optimization of CIES. This paper proposes a risk-managed operation optimization method based on portfolio theory for the CIES in day-ahead and real-time electricity markets. First, both kinds of markets are modeled as risky investment markets, whose returns and risks are described by the mean value and the variance of the electricity price. Second, the power purchase of the CIES from different markets is modeled as risky investment activities, in which the Markowitz portfolio theory is employed to balance the risk and cost. Third, the optimal portfolio can be obtained by solving the operation optimization problem of the CIES considering the risk preference of the owner. The case studies show that the CIES can reduce price volatility by 10.11% with only a 3.46% increase in cost in the day-ahead market. The proposed method provides a balance between different returns and risk fluctuations according to user preferences, which verified the validity and feasibility.

Operational optimization of copper flotation process based on the weighted Gaussian process regression and index-oriented adaptive differential evolution algorithm

Concentrate copper grade (CCG) is one of the important production indicators of copper flotation processes, and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation industrial processes. This paper addresses the fluctuation problem of CCG through an operational optimization method. Firstly, a density-based affinity propagationalgorithm is proposed so that more ideal working condition categories can be obtained for the complex raw ore properties. Next, a Bayesian network (BN) is applied to explore the relationship between the operational variables and the CCG. Based on the analysis results of BN, a weighted Gaussian process regression model is constructed to predict the CCG that a higher prediction accuracy can be obtained. To ensure the predicted CCG is close to the set value with a smaller magnitude of the operation adjustments and a smaller uncertainty of the prediction results, an index-oriented adaptive differential evolution (IOADE) algorithm is proposed, and the convergence performance of IOADE is superior to the traditional differential evolution and adaptive differential evolution methods. Finally, the effectiveness and feasibility of the proposed methods are verified by the experiments on a copper flotation industrial process.

Multiobjective Operation Optimization of Wastewater Treatment Process Based on Reinforcement Self-Learning and Knowledge Guidance.

This article proposes a multiobjective operation optimization method based on reinforcement self-learning and knowledge guidance for quality assurance and consumption reduction of wastewater treatment process (WWTP) with nonstationary time-varying dynamics. First, operation optimization models are developed by online sequential random vector functional-link (OS-RVFL) neural network, which can realize online sequential learning of model parameters. Then, a knowledge base is established to store typical optimization cases for knowledge guiding the subsequent optimizations. Based on it, a reinforcement self-learning-based multiobjective particle swarm optimization (RSL-MOPSO) algorithm is proposed to perform optimization calculation. In this algorithm, reinforcement self-learning is used for interaction learning between environment and action in optimization, and the particle motion trend of algorithm is adjusted according to the feedback information of the optimization process. The effects of wastewater state parameters on particles are recorded and reused to improve the solution quality and calculation efficiency of optimization. Moreover, to make good use of the information of the previous optimizations and balance the coordination between global search in the early stage and local search in the later stage, a selective information feedback mechanism is further proposed to ensure the diversity and convergence of the algorithm. Finally, prediction-based intelligent decision making is performed to select the final optimization solution as the final setpoints for the lower-level controllers from the Pareto frontier with considering specific technical requirements. Data experiments show that the proposed method can effectively reduce energy consumption and ensure effluent quality.

Hierarchical Operation Optimization for Regenerative Braking Energy Utilizing in Urban Rail Traction Power Supply System

The energy feedback system (EFS) is widely accepted to utilize the regenerative braking energy (RBE) in an urban rail traction power supply system (TPSS). However, the sharing relationship of RBE between EFS, traction trains and on-board braking resistors is not clear. In addition, the impact of EFS operation on the sharing of RBE has been rarely studied. This paper proposed a hierarchical operation optimization method for improving the utilization of shared RBE in TPSS through the EFS. An optimizing model for the dynamic start-up voltage threshold of EFS is established, with the objective of minimizing TPSS power consumption. A fast prediction model of train operation information is developed to analyze the steady-state power flow in advance. The optimal solution is searched using a salp swarm algorithm (SSA) on a per second basis. A microsystem of three traction stations and two trains is analyzed. Compared to the conventional constant voltage operation scheme, the optimal solution achieves a maximum additional energy-saving efficiency improvement of 2.44%. Efficient sharing of RBE is identified as the key to achieving energy savings. Regarding the local control part, system stability analysis is verified. Real-time simulation results indicate that the dynamic operating mode of EFS efficiently distributes RBE.