Optimal Dispatch Research Articles

A Novel Time Evolution-Based Cascading Failure Model Considering Auto-Reclosers

The auto-recloser (AR) contributes to the service restoration of the transmission lines tripped by overload and relay protection mal-operation, which may complicate cascading outage processes. This brief develops a novel model to investigate the impact of ARs on cascading failure (CF) propagation in power systems with the representation of temporal evolution. The cascading outage processes with and without ARs are first compared in a timeline manner to highlight the function of ARs. Then, combined with the inverse time and hidden failure characteristics of overcurrent protections, the CF model considering ARs is established to elaborate the cascading propagation and reveal the reclosing effect. Finally, the time-constrained optimal dispatching strategy is proposed to block CFs in time before the next line excision. Case studies on IEEE 118-bus and IEEE 300-bus systems demonstrate the feasibility and effectiveness of the proposed method.

Synergetic Scheduling Energy and Reserve of Wind Farms for Power Systems with High-Share Wind Power

With wind and other variable renewable energy (VRE) generation gradually becoming the main power source, it is difficult to ensure the flexible and secure operation of the system by completely relying on conventional sources to balance the uncertainty and volatility of VRE. The increase of VRE penetration leads to the shortage of operation reserves, and wind power is allowed to participate in reserve services. However, the output of wind power is uncertain, and how to ensure the reliability and sustainability of wind power reserves is still an open question. A robust energy and reserve dispatch method considering the flexibility of wind power is proposed to solve this problem. To effectively measure the output and reserve margin of wind power under uncertain scenarios, (1) the grid connection coefficient of wind turbine generators (WTGs) is defined, and the uncertainty set considering active wind power curtailment is established; and (2) based on two kinds of WTG control methods, the reserve model of wind farms is established. The model links the reserve capacity between the base scenario and uncertain scenarios through the reserve coefficient, which ensures that the wind power reserve determined in the base scenario is still deliverable in any uncertain scenario. The robust scheduling model determines the optimal dispatching and reserve plan of conventional units and wind farms by balancing the system operation economy and the schedulable reserve of wind power. The column-and-constraint generation algorithm is used to solve the model. The effectiveness of the proposed model is verified by the illustrative results of the IEEE normal system.

Hidden Markov Chain Model-Based Intelligent Distributed Optimization Scheduling Operation Error Prevention Method for Main Network

After the implementation of the intelligent distributed optimal dispatching mode of the main network, the number of operations is more frequent, resulting in the problem of operation error, which affects the reliability and stability of the power grid operation. In this paper, a hidden Markov chain (HMC) model-based intelligent distributed optimization scheduling operation error prevention method for the main network is proposed. In this study, the power grid topology model is established through continuous access, which lays the foundation for the dynamic tracking of dispatching operation instructions. The hidden Markov chain model is used to estimate the operational risk of scheduling. Combining with the association mining theory, the anti-error detection rules between risk and anti-error strategy are established, and the anti-error strategy is implemented. The results show that under the application of the operation error prevention method, the operation error prevention response time difference and the voltage fluctuation range of the power grid reach the minimum. Through this study, it is expected to provide a reliable reference for dispatchers to make more effective dispatching decisions and correct dispatching errors in time.

Optimal dispatch of electricity considering shared source-load carbon responsibility

Optimal dispatch of electricity considering shared source-load carbon responsibility

Optimal Dispatch of Multi-energy Microgrid Considering Flexible Resources Aggregation

Distributing energy has facilitated the interaction between electricity, heating, and gas systems. Aggregating flexible resources in the optimal scheduling process of multi-energy microgrids can promote the complementary coordination of multi-energy systems and reduce useless energy waste. In this paper, flexible resource aggregation analysis is added to the optimal scheduling problem of multi-energy microgrids. Firstly, a resource aggregation model based on Mincowski addition is proposed to fully characterize the schedulable potential of flexible resources. Then, a multi-energy microgrid collaborative optimal scheduling model is established to fully exploit the operational potential of flexible resources. Finally, a numerical example is simulated to verify the validity of the aggregation model and control algorithm above-mentioned, which improves the efficiency and accuracy of optimal control of multi-energy microgrids.

Quantum behaved artificial bee colony based conventional controller for optimum dispatch

<p>Since a multi area system (MAS) is characterized by momentary overshoot, undershoot and intolerable settling time so, neutral copper conductors are replaced by multilayer zigzag graphene nano ribbon (MLGNR) interconnects that are tremendously advantageous to copper interconnects for the future transmission line conductors necessitated for economic and emission dispatch (EED) of electric supply system giving rise to reduced overshoots and settling time and greenhouse effect as well. The recent work includes combinatorial algorithm involving proportional integral and derivative controller and heuristic swarm optimization; we say it as Hybrid- particle swarm optimization (PSO) controller. The modeling of two multi area systems meant for EED is carried out by controlling the conventional proportional integral and derivative (PID) controller regulated and monitored by quantum behaved artificial bee colony (ABC) optimization based PID (QABCOPID) controller in MATLAB/Simulink platform. After the modelling and simulation of QABCOPID controller it is realized that QABCOPID is better as compared to multi span double display (MM), neural network based PID (NNPID), multi objective constriction PSO (MOCPSO) and multi objective PSO (MOPSO). The real power generation fixed by QABCOPID controller is used to estimate the combined cost and emission objectives yielding optimal solution, minimum losses and maximum efficiency of transmission line.</p>

Optimal defense strategy for AC/DC hybrid power grid cascading failures based on game theory and deep reinforcement learning

This paper proposes a two-person multi-stage zero-sum game model considering the confrontation between cascading failures and control strategies in an AC/DC hybrid system to solve the blocking problem of DC systems caused by successive failures at the receiving end of an AC/DC system. A game model is established between an attacker (power grid failure) and a defender (dispatch side). From the attacker’s perspective, this study mainly investigates the problem of system line failures caused by AC or DC blockages. From the perspective of dispatch-side defense, the multiple-feed short-circuit ratio constraint method, output adjustment measures of the energy storage system, sensitivity control, and distance third-segment protection adjustment are used as strategies to reduce system losses. Using as many line return data as possible as samples, the deep Q-network (DQN), a deep reinforcement learning algorithm, is used to obtain the Nash equilibrium of the game model. The corresponding optimal dispatch and defense strategies are also obtained while obtaining the optimal sequence of tripping failures for AC/DC hybrid system cascading failures. Using the improved IEEE 39-node system as an example, the simulation results verify the appropriateness of the two-stage dynamic zero-sum game model to schedule online defense strategies and the effectiveness and superiority of the energy storage system participating in defense adjustment.

Optimal load dispatch of multi-source looped district cooling systems based on energy and hydraulic performances

Multi-source looped district cooling (MLDC) systems exhibit promising application potential owing to their ability to utilize various renewable energy resources. The load dispatch for multiple sources is considered a critical factor during operation owing to its effects on the energy and hydraulic performance. However, few studies have investigated the behavior of energy and hydraulic performance under different load dispatch ratios (LDRs). In addition, straightforward methods for determining the optimal load dispatch of MLDC systems have not been introduced. This study investigated the relationship between the load dispatch and operating performance and proposed a fundamental method to achieve optimal load dispatch for MLDC systems and overcome the existing challenges. A double-source looped district cooling system was selected as the case study, and its Modelica model was developed. Subsequently, we investigated the relationship between the LDRs and operating performance, and verified the existence of an optimal LDR (LDRopt), which achieves the best hydraulic performance and lowest energy consumption. Finally, a dataset constructed using a hybrid optimization algorithm was employed to develop a multi-variable data-driven model for predicting LDRopt under various load conditions. This study provides a valuable framework and recommendations for further investigation of the optimal control of MLDC systems.

Optimal sizing and dispatch of solar power with storage

Optimal sizing and dispatch of solar power with storage

Bilevel Optimal Economic Dispatch of CNG Main Station Considering Demand Response

Compressed natural gas (CNG) main stations are critical components of the urban energy infrastructure for CNG distribution. Due to its high electrification and significant power consumption, researching the economic operation of the CNG main station in demand response (DR)-based electricity pricing environments is crucial. In this paper, the dehydration process is considered in the CNG main station energy consumption model to enhance its participation in DR. A bilevel economic dispatch model for the CNG main station is proposed, considering critical peak pricing. The upper-level and lower-level models represent the energy cost minimization problems of the pre-system and rear-system, respectively, with safety operation constraints. The bilevel programming model is solved using a genetic algorithm combined with a bilevel programming method, which has better efficiency and convergence. The proposed optimization scheme has better control performance and stability, reduces the daily electricity cost by approximately 21.04%, and decreases the compressor switching frequency by 50.00% without changing the CNG filling demand, thus significantly extending the compressor’s service life. Moreover, the average comprehensive power cost of processing one unit of CNG reduces 20.62%.

A competitive swarm optimizer with local search for solving optimal reactive power dispatch of wind farm

A competitive swarm optimizer with local search for solving optimal reactive power dispatch of wind farm

Multi-Time-Scale Coordinated Optimum Scheduling Technique for a Multi-Source Complementary Power-Generating System with Uncertainty in the Source-Load

An optimal dispatching strategy for a multi-source complementary power generation system taking source–load uncertainty into account is proposed, in order to address the effects of large-scale intermittent renewable energy consumption and power load instability on power grid dispatching. The uncertainty problem is first converted into common situations for study, such as load power forecasting and solar and wind power. The backward scenario reduction and Latin hypercube sampling techniques are used to create these common situations. Based on this, a multi-timescale coordinated optimum scheduling control method for a multi-source complementary power generation system taking the demand response into account is presented, and the optimal operation of a wind–PV–thermal-pumped storage hybrid system is examined. The time-of-use power price optimizes the electrical load in the day-ahead pricing mode, and the two types of demand response loads are selected in the day-ahead scheduling. Second, the lowest system operating cost and the minimal day-ahead and intra-day adjustment of each source are established as the optimization targets in the day-ahead and intra-day phases of the multi-timescale coordinated scheduling model of the multi-source complementary system. The example study demonstrates that the scheduling strategy may increase the amount of renewable energy consumed, minimize load fluctuations, increase system stability, and further reduce operating expenses, proving the viability and efficiency of the suggested strategy.

Energy Management System (EMS) Based on Model Predictive Control (MPC) for an Isolated DC Microgrid

Microgrids have become an alternative for integrating distributed generation to supply energy to isolated communities, so their control and optimal management are important. This research designs and simulates the three levels of control of a DC microgrid operating in isolated mode and proposes an Energy Management System (EMS) based on Model Predictive Control (MPC), with real-time measurement feedback for optimal energy dispatch, which ensures power flow distribution and operation at minimum cost while extending the lifespan of the BESS. The EMS can react to disturbances produced in the lower control levels. The microgrid’s performance is analyzed and compared in two scenarios without EMS, and with EMS against changes in irradiation and changes in electricity demand. The fulfillment of the power balance is evaluated by analyzing the power delivered by each generation unit, the operating cost, and the state of charge of the battery (SOC).

An Improved Deep Reinforcement Learning Method for Dispatch Optimization Strategy of Modern Power Systems

As a promising information theory, reinforcement learning has gained much attention. This paper researches a wind-storage cooperative decision-making strategy based on dueling double deep Q-network (D3QN). Firstly, a new wind-storage cooperative model is proposed. Besides wind farms, energy storage systems, and external power grids, demand response loads are also considered, including residential price response loads and thermostatically controlled loads (TCLs). Then, a novel wind-storage cooperative decision-making mechanism is proposed, which combines the direct control of TCLs with the indirect control of residential price response loads. In addition, a kind of deep reinforcement learning algorithm called D3QN is utilized to solve the wind-storage cooperative decision-making problem. Finally, the numerical results verify the effectiveness of D3QN for optimizing the decision-making strategy of a wind-storage cooperation system.

Energy Dispatch for CCHP System in Summer Based on Deep Reinforcement Learning

Combined cooling, heating, and power (CCHP) system is an effective solution to solve energy and environmental problems. However, due to the demand-side load uncertainty, load-prediction error, environmental change, and demand charge, the energy dispatch optimization of the CCHP system is definitely a tough challenge. In view of this, this paper proposes a dispatch method based on the deep reinforcement learning (DRL) algorithm, DoubleDQN, to generate an optimal dispatch strategy for the CCHP system in the summer. By integrating DRL, this method does not require any prediction information, and can adapt to the load uncertainty. The simulation result shows that compared with strategies based on benchmark policies and DQN, the proposed dispatch strategy not only well preserves the thermal comfort, but also reduces the total intra-month cost by 0.13~31.32%, of which the demand charge is reduced by 2.19~46.57%. In addition, this method is proven to have the potential to be applied in the real world by testing under extended scenarios.

Consensus-Based Distributed Optimal Dispatch of Integrated Energy Microgrid

In recent years, the energy form of microgrids is constantly enriching, while the decentralization requirements of microgrids are constantly developing. Considering the economic benefits of an integrated energy microgrid (IEM), this paper focuses on the distributed optimal dispatch of IEM based on a consensus algorithm. The microgrid structure and multi-agent system are combined organically to get the decentralized architecture of IEM. This paper takes the incremental cost rate of each unit in IEM as a consensus variable. Based on the consensus theory, iterative optimization is carried out to achieve the optimal economic operation and power supply-demand balance of IEM. The distributed optimal dispatch is realized, and the convergence of the algorithm is proved. The experiment is carried out with LabVIEW and MATLAB and verifies the effectiveness of the algorithm. The results show that the distributed optimal dispatch algorithm can effectively reduce the power generation cost of the integrated energy system.

Solution to the deterministic and stochastic Optimal Reactive Power Dispatch by integration of solar, wind-hydro powers using Modified Artificial Hummingbird Algorithm

In electrical power networks, the optimal reactive power dispatch (ORPD) problem is essential to the system studies to perform reliable and secure operations by maintaining the control variable within their permissible limits. An electric network consisting of thermal generators has been studied widely for optimal power dispatch problems. Increasing renewable energy resources (RERs) penetration into the electric power grid required power flow studies while integrating these resources. It is a strenuous task to incorporate renewable energy resources into the ORPD problem due to the stochastic nature of RERs. This paper solved the stochastic optimal reactive power dispatch (ORPD) problem considering the uncertainties of renewable resources such as; solar PV, wind turbine, and hydropower generation systems. The time-varying load demand and the power generated from the renewable energy resources are represented using the normal, the lognormal, the Weibull, and the Gumbel probability density function (PDFs). Then, the Monte Carlo simulations reduction of scenarios-based technique is applied to generate a suitable number of scenarios. The second contribution presents an efficient version of the Artificial Hummingbird Algorithm (MAHA) for solving the Stochastic and Non-Stochastic ORPD. The proposed MAHA is based on the levy flight motion and the distance bandwidth motion around the best solution to enhance the exploration and exploitation behavior of the traditional AHA to avoid trapping into the local minima. The proposed algorithm is validated and tested on the IEEE 30-bus system for active power loss reduction, voltage profile improvement, and voltage stability enhancement. The results showed the effectiveness and superiority of the proposed MAHA for solving the ORPD problem compared to the well-known conventional algorithms such as AHA, GWO, SCA, DO, BWO and other state-of-the-art algorithms.

Improved gravitational search algorithm and novel power flow prediction network for multi-objective optimal active dispatching problems

Multi-objective optimal active dispatching (MO-OAD) is a high-profile topic in power grid optimizations. Traditional methods are insufficient for the MO-OAD problem due to its nonconvexity and strict constraints. Therefore, an improved multi-objective gravitational search algorithm (IMGSA) with better population diversity and search capability is put forward in this paper. Five MO-OAD experiments considering various user requirements demonstrate that the IMGSA achieves preferable Pareto-front (PF) over the non-dominated sorting genetic algorithm-II (NSGA-II) and other recently published algorithms. More importantly, to further explore potential elite schemes after obtaining the best compromise scheme (BCS) of IMGSA, an efficient power flow prediction model based on the radial basis function (RBF) network is proposed for the first time. By accurately mapping the nonlinear relationship between the control variables of MO-OAD and optimization objectives, more than five elite dispatching schemes are determined by the suggested RBF prediction model. These elite schemes greatly reduce the fuel cost, power loss and exhaust emission of different IEEE power systems. Quantitative evaluations such as generational distance (GD) and hyper-volume (HV) indicators prove that the proposed IMGSA with RBF prediction model (IMGSA-RBF) is more competitive than many existing algorithms. Detailed MO-OAD experiments show that IMGSA-RBF method has significant advantages in PF-uniformity, PF-diversity, and the quality of optimal dispatching schemes. In general, the innovative IMGSA-RBF method provides a valuable technology to realize desirable power grid operation with less carbon emission and better economy.

Ingenuity of Shannon entropy-based fractional order hybrid swarming strategy to solve optimal power flows

An important issue for researchers looking into the effectiveness of power distribution systems is the Optimal Reactive-Power Dispatch (ORPD) problem, which aims to reduce real power line losses. Numerous techniques have been created with the express purpose of improving upon the optimization method's performance in tuning operational variables and exploring it through the estimation of results. Using entropy evolution (EE) and fractional calculus (FC) concepts, the research presents a novel method that is implemented in the hybrid meta-heuristic computational paradigm of Moth Flame Optimization (MFO) and Particle Swarm Optimization (PSO) algorithms for the ORPD problem. These techniques were incorporated into the MFO-PSO algorithm to enhance its memory effect, robustness, and stability. This was done to address the MFO-PSO vulnerability. ORPD problem using the IEEE-57 bus and IEEE-118 bus standards are used to test the novel Entropy design MFO and Fractional-order PSO algorithms as (EMFO-FPSO). The superiority of the proposed EMFO-FPSO algorithm has been demonstrated through a comparative analysis study with well-known optimizer solvers from the literature.

Low-carbon economic dispatch of integrated energy system considering carbon trading mechanism and LAES-ORC-CHP system

Under the theme of low carbon, in order to improve the economy of integrated energy system (IES), optimize the operational flexibility of equipment and further reduce the carbon emission level of IES, a low-carbon economy operation strategy of the IES is proposed. First, the IES is considered to participate in the carbon trading market, and the carbon trading mechanism is introduced to control the carbon emissions of the system. Second, on the basis of the working characteristics of liquid air energy storage (LAES), organic Rankine cycle (ORC), and combined heat and power (CHP), the LAES-ORC-CHP system is established in the IES to achieve flexible supply of heat and power. Then, aiming at minimizing the sum of system operation costs and carbon transaction costs, the low-carbon economy optimal dispatch model of IES is proposed. Finally, the effectiveness of the proposed model is verified by setting multiple operation scenarios. Simulation results show that the proposed model can fully consider and balance the economy and low carbon level of the system and provide a reference for the low-carbon economic dispatch operation of the IES.