Economic Dispatch Method Research Articles

A method of short-term risk and economic dispatch of the hydro-thermal-wind-PV hybrid system considering spinning reserve requirements

• A framework for short-term risk and economic dispatch of HTWPS considering spinning reserve is proposed. • Synchronic peak-shaving strategy is proposed to reduce hydropower fluctuation and improve hydropower peak shaving. • SR reasonable arrangement is helpful to reduce the risk and cost of the hybrid system. • The incoming water and comprehensive utilization significantly affect the risk and economy of HTWPS. The objective of the traditional dispatching model of multi-energy systems is to seek the most economical scheduling mode, while ignoring the operating risk caused by inaccurate predictions of wind and PV power. Given this, this paper proposes a framework for the short-term risk and economic dispatch of the hydro-thermal-wind-PV hybrid system (HTWPS). First, a quantile regression method and multivariate Gaussian distribution are adopted to generate wind-PV scenarios. Then, a short-term risk and economic dispatching model of the HTWPS considering spinning reserve (SR) requirements is established to minimize the operating risk and cost. Finally, a synchronic peak-shaving strategy for hydropower stations is developed to determine reasonable hydropower generation plans in model solving. The proposed framework is applied to China’s Qinghai grid. The results show that: (1) As comprehensive utilization flow rises, the cost and risk drop sharply in the dry season, but almost keep unchanged in the wet season constrained by hydropower SR boundary. (2) The power abandonment risk in the dry season is high due to the low hydropower generation capacity induced by limited runoff. The power abandonment and shortage risk are mainly subject to the hydropower SR boundary in the wet season. (3) Compared with the operating mode with hydropower SR alone, the comprehensive risk decreases by 10.6%, and the comprehensive cost rises by 5.8% under the operating mode with hydro and thermal power SR (HTSR) in the dry season. The comprehensive risk decreases by 51.4%, and the comprehensive cost increases by 172.8% under HTSR in the wet season.

Weighted matrix based distributed optimization method for economic dispatch of microgrids via multi-step gradient descent

Due to the slow convergence rates and reliance on global information, distributed optimization methods, such as alternating direction method of multipliers (ADMM) and distributed gradient descent method (DGDM), have been difficult to meet the needs of solutions for the large-scale distributed system. In this paper, a distributed multi-step gradient descent method (DMGDM) combined with the allocation of upper bounds of second derivatives, has been proposed to tackle the economic dispatch problem (EDP) of microgrid (MG). At first, the agent assigns the reciprocal of its own upper bound of the second derivative according to the product of out-degree and upper bounds of second derivatives from the neighbors, which is the key to our method. Further, the weight matrix is constructed through the negotiation among neighbors in a distributed manner. Additionally, the distributed weight matrix relaxes the convergence conditions of the proposed method so that momentum parameters can be tuned without the need for global information. Numerical examples show that the convergence rate of our method is faster than the ADMM and the DGDM. Finally, a bi-layer optimization model of the EDP of MG is built via Matlab/Simulink, and the results show that the proposed method can realize the optimal dispatch of controllable distributed generators (DGs) in the MG.

Economic Dispatch of Power Retailers: A Bi-Level Programming Approach via Market Clearing Price

For power retailers in a smart grid, it is necessary to design an economic dispatch method to maintain a balance between power supply and demand on the sale side as well as obtain better economic benefits. This study concentrates on the economic dispatch of the dominant retailer in a regional market. The dominant retailer is considered to be equipped with generator resources such as distributed photovoltaics (PV), wind turbines (WT), and microturbines (MT). As one retailer cannot exactly predict the market conditions of other retailers, the retail market is considered to be modeled as a dichotomous-market model consisting of the dominant retailer market and the other retailers market. As a result, a bi-level optimal dispatch model is proposed for the dominant power retailer based on the dichotomous-market model. In the proposed model, the outer problem aims to minimize the costs of purchases under time-of-use (TOU) price given in the market clearing process, while the inner problem is formulated to simulate the process of market clearing. Furthermore, the bi-level model is converted to a single-level model via the Karush–Kuhn–Tucker (KKT) conditions and eventually solved by employing the YALMIP toolbox with Gurobi solver. Finally, a case study is conducted to validate the effectiveness and adaptability of the proposed model, and the analysis of the variables is presented.

Fast distributed gradient descent method for economic dispatch of microgrids via upper bounds of second derivatives

Distributed optimization algorithms, such as alternating direction method of multipliers (ADMM) and distributed gradient descent method (DGDM), can no longer meet the needs of the application in the large system due to their slow convergence rates. In this paper, based on the upper bounds of second derivatives, a fast distributed gradient descent method (FDGDM) has been proposed, where the upper bounds of second derivatives and out-degree information are exchanged among the neighbors, and the weight matrix used for iteration is completely distributedly formed. Especially, the objective function keeps declining monotonically during the iterative process, which is the key to accelerating the convergence rate of our method while maintaining the constraints. Numerical experiments are designed to verify the faster convergence rate of the FDGDM compared with the ADMM. Finally, a microgrid (MG) simulation platform based on Matlab/Simulink is established, and our method is applied to solve the economic dispatch problem (EDP) of MG. The results demonstrate that the proposed method can achieve the optimal economic dispatch while ensuring the operation stability.

Integrated unit commitment and economic dispatch of combined heat and power system considering heat-power decoupling retrofit of CHP unit

• A detailed HPD model on the power generation side of CHP unit is proposed. • A simulation method for UCED considering HPD flexibility retrofit is established. • The heat delay and heat loss are considered in the proposed UCED method. • The proposed model and method are verified in IEEE RTS-79 test system and applied to a practical case in China. • Some guidance and suggestions are proposed for the flexibility retrofit. Low-carbon development of a power system is realised via the flexibility retrofit of thermal power units with heat-power decoupling (HPD) of combined heat and power (CHP) units providing the necessary transformation pathway. First, this study examined the primary measures of HPD of CHP units and their impact on unit flexibility. Thereafter, aiming to realise flexibility retrofit of thermal power units, a detailed HPD model on the power side was proposed. In addition, the heat delay and heat loss in the process of heat transmission were also considered, followed by the establishment of a simulation method for unit commitment and economic dispatch (UCED) of CHP system considering HPD. Subsequently, based on IEEE RTS-79 test system, the effectiveness of the model and method proposed in this study was proven. Moreover, the proposed UCED method was applied to systematically analyse the comprehensive benefits achieved via the use of the HPD retrofit of the CHP units. The results showed that HPD retrofit has significant benefits in reducing dispatch costs, enhancing units' flexibility, and promoting wind and solar energy consumption. Finally, combined with the actual power supply and heating in Zhangjiakou, China, the HPD effect in the area was analysed to provide guidance and suggestions for the flexibility retrofit of local thermal power plants.

Improved approximate dynamic programming for real-time economic dispatch of integrated microgrids

Economic dispatch of electricity-heat microgrid is critical for real-time power generation and storage. However, conventional economic dispatch algorithms are generally integrated with static unit models without considering dynamics of units, thus leading to difficulties for real deployment in stochastical environments. In this paper, we propose a novel approximate dynamic programming (ADP) based real-time optimization algorithm. Specifically, the proposed ADP is employed to solve the Markov decision process with considering the dynamic process of combined-cycle gas turbine. Furthermore, we also design a novel weighted piecewise linear function to achieve the near-optimal solution, which is simple but effective for computational complexity reduction. In the experimental section, we conduct extensive experiments with comparisons to other economic dispatch methods. The experimental results indicate that: 1) The dynamic process of energy conversion brings more practical solutions; 2) The proposed ADP-based method could handle the stochasticity of the microgrid; 3) The proposed method outperforms the other intra-day optimization policies in both economical and computational efficiency. • Propose real-time optimization for an integrated microgrid with uncertainties. • Dynamic model of CCGT plant makes solutions more practical. • Dispatch flexibilities of electricity and heat storage are jointly utilized. • Improved updating method enhances the convergence performance of ADP. • Comprehensive experiments validate the proposed ADP.

Low-carbon economic dispatch of power system based on mobile hydrogen storage

To alleviate the global warming crisis, carbon reduction is an inevitable trend of sustainable development. The energy carrier with Hydrogen (H2) is considered to be one of the promising choices for realizing a low-carbon economy. With the increasing penetration level of wind power generation and for well-balancing wind generaion fluctuations, this paper proposes a low-carbon economic dispatch method for power systems based on mobile hydrogen storage(MHS). The wind power surplus during off-peak load periods is first utilized to generate green H2. Afterward, the green H2 is optimally transported to multiple hydrogen storage(HS) stations for generating power electricity by flexibly controlling the electrolysis(EL), methanation(ME), carbon capture(CCS) and H2 power generation processes, in such a way the wind power is coordinated with the hydrogen production, transport and utilization to reduce the total carbon emission and minimize the operation cost of power systems. Finally the proposed power system low-carbon economic dispatch model is verified by case studies.

Distributionally Robust Optimal Dispatching of CHP Microgrid considering Concentrating Solar Power and Uncertainty

In order to solve the problem of diversified low-carbon energy supply with renewable energy as the main body, concentrating solar power (CSP) stations are introduced to act as cogeneration units. Taking full advantage of the flexible coupling and multienergy complementarity of electric, heat, and gas, an economic dispatch method for combined heat and power microgrid systems (CHP microgrid) with interconnected electric, heat, and gas is proposed. First, build the CSP-CHP microgrid structure and model the main equipment. Then, aiming at the minimum operating cost of the system, a regular scheduling model of the CSP-CHP microgrid system is established. On this basis, in order to deal with the uncertainty of renewable energy output, a distributionally robust optimization (DRO) model is introduced. In the DRO model, the Kullback–Leibler (KL) divergence is used to construct an ambiguity set about the predicted error of renewable energy output, and finally, the CSP-CHP microgrid DRO economic dispatch model is established. Finally, the system is simulated and analyzed in a typical CSP-CHP microgrid system, and the feasibility and effectiveness of the proposed method are verified by analysis. In addition, the necessity of introducing CSP and the advantages of the DRO model is further explained by comparison.

An Economic Dispatch Method of Microgrid Based on Fully Distributed ADMM Considering Demand Response

Aiming at the problem that the existing alternating direction method of multipliers (ADMM) cannot realize totally distributed computation, a totally distributed improved ADMM algorithm that combines logarithmic barrier function and virtual agent is proposed. We also investigate economic dispatch for microgrids considering demand response based on day-ahead real-time pricing (RTP), which forms a source-load-storage collaborative optimization scheme. First, three general distributed energy sources (DERs), renewable energy resources (RESs), conventional DERs and energy storage systems (ESSs), are considered in the method. Second, the goal of economic dispatch is to minimize the sum of three energy generation costs and implement the optimal power allocation of dispatchable DERs. Specifically, the approach not only inherits the fast computational speed of ADMM but also uses barrier function and virtual agent to handle inequality and equality, respectively. Moreover, the approach requires no coordination center and only the communication between current agent and adjacent agent to achieve totally distributed solution for every iteration, which can preserve information privacy well. Finally, a 30-node microgrid system is used for case analysis, and the simulation results demonstrate the feasibility and effectiveness of the proposed approach. It can be found that, the proposed approach converges to the optima when p = 0.01, v = 100, t0 = 0.01 and μ = 2.

A data-enhanced distributionally robust optimization method for economic dispatch of integrated electricity and natural gas systems with wind uncertainty

With growing penetrations of wind power in electricity systems, the coordinated dispatch of integrated electricity and natural gas systems is becoming a popular research topic. Distributionally robust optimization can cope with the wind uncertainty of integrated electricity and natural gas systems by providing optimal solutions for the worst-case probability distribution. However, limited historical wind data hinder the estimation of worst-case probability distribution. As a breakthrough in artificial intelligence, generative adversarial networks can be established to approximate a complex uncertain probability distribution from raw data and generate realistic data subject to the identical distribution. This paper proposes a data-driven optimization method for economic dispatch of integrated electricity and natural gas systems with wind uncertainty, whose probability distribution is free. Based on limited historical data, the data-driven generative adversarial network generates artificial wind power data, which helps to improve the estimation of worst-case probability distribution in distributionally robust optimization. Moreover, the robustness of optimization solutions can be adjusted cost-effectively by controlling the auxiliary data number. In a case study, optimization solutions of the proposed method are shown to achieve a lower probability of chance constraint violation at a nearly negligible cost increase compared with those from four typical optimization methods.

Bus-Privacy-Preserving Distributed Economic Dispatch Method for Power Systems Using Iteration Functions

The sufficiency of electricity market competition and the economy of power system operation require that the economic dispatch should not only preserve the bus privacy but consider network losses. The existing economic dispatch methods, however, can’t preserve the privacy of each bus from leakage and even complicate the solution when they reasonably consider network losses, due to the coupling complexity of network losses. To address these issues, this paper presents a novel distributed economic dispatch (DED) method based on KKT optimality conditions and iteration functions. It focuses on an economic dispatch model that contains bus active power balance equations and generator power limit inequalities. First, the model’s KKT optimality conditions with inequalities are simplified to equivalent pure equality-type optimality conditions. Then a set of iteration functions is created using the Taylor series, which overcomes the difficulty caused by the fact that unknown bus voltage phases are completely contained in the sine and cosine functions of optimality conditions. Finally, a fast fixed-point iteration procedure is proposed for bus agents to solve the economic dispatch model. The proposed method reasonably considers network losses by using the set of bus active power balance equations. It not only preserves the privacy of each bus from leakage but is straightforward in solution (does not complicate the solution). In addition, it is fully bus-level distributed. Numerical simulation results of systems with different sizes verified the effectiveness and advantages of the proposed method.

A Fully Distributed Economic Dispatch Method in DC Microgrid Based on Consensus Algorithm

Uncertainties of intermittent renewables and complexities of load demands bring challenges to the optimal operation of the microgrid. This paper proposes a fully distributed method based on consensus algorithm for solving the economic dispatch problem (EDP) of DC microgrid. The proposed distributed method is performed using multiagent system. It only needs information from neighboring agents and does not need a central controller. A two-level control structure is adopted to realize the economic dispatch and voltage regulation simultaneously. The upper level obtains the optimal power reference of dispatchable agents and maintains the power balance of the system. The lower level performs an improved droop control to track the power reference and regulate the average bus voltage for the microgrid. The proposed method’s properties and convergence are analyzed using eigenvalue perturbation theory to obtain a faster convergence speed. By designing the adjacency matrix appropriately, the proposed method can ensure the system stable when the communication topology changes. Finally, several simulations are designed to verify the effectiveness of this novel control method.

Multi-objective economic load dispatch method based on data mining technology for large coal-fired power plants

Coal-fired power plants occupy a dominant position in the power system and are the key sector of energy consumption and pollutant emission. A fallacious load dispatching scheme for the power plant will have a negative impact on energy use and emission reduction. This paper proposes a multi-objective economic load dispatch method for the coal-fired power plant based on data mining technology. The core of the method is to mine the optimal decision-making samples from the offline database to guide the online economic load dispatching, according to the load demand of the power grid. Thus, in the big data environment, a hierarchical clustering and retrieval strategy based on the fuzzy c-means clustering algorithm is proposed to construct the offline database, which can lower the online calculation amount of sample retrieval while balancing the retrieval accuracy. Moreover, the multiple attribute decision-making method, which comprehensively considers multiple objectives, is used to mining the optimal decision-making samples from the offline database to guide the load dispatching scheme for the power plant. The database maintenance strategy is utilized to optimize and update the offline database. Finally, the proposed method is applied to an on-duty coal-fired power plant to verify its effectiveness. The results show that the data-mining-based method can achieve the plant-level optimal load dispatching while meeting the actual requirements of the grid. • The hierarchical clustering retrieval is designed to reduce calculation. • The multiple attribute decision-making is used to mining the optimal samples. • The proposed data-based method is applied to a coal-fired power plant.

A Stochastic Economic Dispatch Method Considering a Voltage-Sensitive Load as a Reserve

The volatility and uncertainty of renewable energy seriously affect the stability of power systems. To overcome this challenge and improve the ability of power systems to deal with disturbances, an optimized dispatch method for a high-permeability renewable energy-integrated power system with a voltage-sensitive load (VSL) as the reserve is proposed. To fully utilize the flexibility of the VSL and the reactive power control capability of the transmission grid, this paper first establishes a voltage control model based on coordinated sensitivity-based transmission and distribution and quantifies the active power adjustment boundary considering voltage stability. Then, based on the corresponding risk, a stochastic reserve decision model of a power system considering the regulation capacity of a VSL is established. A case study of a modified IEEE RTS-79 system shows that the method can fully consider the influence of the voltage at the access point on the load power, change the power of the VSL, and provide an active power reserve for the power system. This approach can reduce the risk of wind curtailment and load shedding in the system and improve the economy and flexibility of system operation.

Economic Dispatch Methods for Smart Grid Based on Improved SPEA2 and Improved NSGA2

The severity of the ongoing environmental crisis has prompted the development of renewable energy generation and smart grids integration. The access of enewable energy makes the economic dispatching of smart grid complicated. Therefore, the economic dispatching model for smart grid is very necessary. This paper presents an economic dispatching model of smart power grid, which considers both economy and pollution emission. The smart grid model used for the simulation is construced of wind energy, solar energy, fuel cell, and thermal power, and the use of fuel cell enables the smart grid to achieve multi-energy complementar. To overcome the defect of the traditional centralized communication methods, which are prone to communication jams, this paper adopts a multi-agent inform ation exchange method to improve the stability and efficiency. In terms of the solution method for this model, this paper proposes Improved Strength Pareto Evolutionary Algorithm 2(ISPEA2) and Improved Non-dominated Sorting Genetic Algorithm 2(INSGA2) that solves the economic dispatch problem of a smart grid. The strength Pareto evolutionary algorithm 2(SPEA2),non-dominated sorting genetic algorithm 2(NSGA2) and the improved algorithms are simultaneously applied to the proposed smart grid model for economic dispatching simulation. The simulation results show that ISPEA2 and INSGA2 are effective. ISPEA2 and INSGA2 have shown improvements over SPEA2 and NSGA2 in accuracy or running times.

Analisis Pengaruh Perubahan Beban Terhadap Biaya Bahan Bakar Pembangkitan Pada PLTU Barru

The effect of changes in load on the cost of fuel generation at PLTU Barru is analyzed using the economic dispatch method to determine the characteristics of input-output and incremental fuel costs. Based on the results of the analysis, it is known that the effect of changes in load on the cost of fuel generation at PLTU Barru tends to be directly proportional, where the higher the load generated, the higher the costs incurred. As in June, to produce 33.85 MW of power the total cost is Rp. 457,687,187.47/hour required. Likewise, if the power at an average load of 33.85 MW increases by 1 MWh it will result in an increase in costs of Rp. 13,306,061.24/MWh, but in certain circumstances such as in November with a power of 29.83 MW it will produce the total cost is quite low at Rp. 337,356,512,63/hour. This situation is caused by fuel consumption in November only amounted to 3029.61 T/h.

An adaptive decentralized economic dispatch method for virtual power plant

• A compact yet efficient decentralized economic dispatch (ED) method is proposed. • The iterative process is theoretically proved to have a guaranteed convergence. • Fault tolerance and privacy awareness are achieved based on the bottom-up style. • The adaptive method is strongly robust to the scale of participants and parameters randomness. This paper introduces a decentralized economic dispatch method and an architecture suitable for the virtual power plant (VPP) aggregating massive distributed energy resources (DERs). The convergence condition is given for quadratic cost functions, and is extended to the case of general increasing function of incremental cost (IC). Further analysis shows that the step of this method is adaptive, which is generated from the bottom up according to the responsiveness of each DER unit (DERU). Combined with the decentralized architecture based on message queue (MQ), the algorithm design considers the hosting mechanism of the coordinator failure, which not only improves the efficiency of calculation and communication without losing privacy-protection, but also makes it more fault-tolerant. The correctness and effectiveness of the method are verified in the case studies. The iterative process can respond and converge quickly when DER units reach capacity limits or devices fail/join. Due to the adaptability of the step, the method has strong robustness to the quantity and parameters randomness of underlying units. Therefore, it can be applied to the VPP with a massive number of DERs in order to get consensus solution by rapid economic dispatch.

Control of Generation Ramping for Power Ststem Security Assessment.(Dept.E)

The problem of control and adjustment of power system generation rampinc capability is formulated as a non-linear multi-objective constrained dispatch recognizing generation cost, transmission losses, generator operating limits, line security limits, and load uncertainly. The paper presents a novel method for economic dispatch employing Bender's Decomposition principle coupled with a successive linearization technique to provide certain generation ramping capable to follow rapidly the unexpected load changes. In such iterative scheme, the non-linear functions may be accommodated in the linear programming using updated linearization about the base case solution. The program used offers a sensitivity analysis which permits an answer to the what if question using about 20 % of computational time required to solve the base case. This property is attractive to power system operator. The proposed concept of power dispatch offers an efficient tool to make an economic-secure decision on whether to buy or sell regulating margin or whether to install new ramping capability.

Research on Optimal Strategy of Peak-shaving of Photovoltaic Grid-connected System Based on Simulated Annealing-Q Learning Algorithm

In order to alleviate the problem of large-scale grid-connected photovoltaics and increase the pressure of grid peak regulation, this paper proposes a dynamic economic dispatch method considering the system peak regulation margin. The system peak shaving margin is proposed to deal with the flexibility problem in economic dispatch and improve the enthusiasm of thermal power unit peak shaving. The economic dispatch model is transformed into a dynamic programming model and solved by the simulated annealing-Q learning algorithm. The analysis of the results shows that the proposed dynamic programming strategy can effectively improve the operating economy of the system and increase the flexibility of peak shaving of thermal power units.

A data-driven approach towards fast economic dispatch in electricity–gas coupled systems based on artificial neural network

Abstract The optimization of electricity–gas coupled systems is typically complicated by the nonconvex relationships such as gas flow equations. Piecewise linearization, despite being one of the few solutions that guarantee exactness and optimality, is often discarded for an exceptionally long computational time. In this paper, we present a novel data-driven approach based on artificial neural networks, to enable fast economic dispatch in electricity–gas coupled systems, by utilizing simulation data from the piecewise-linearization-based model-driven method. Load profiles at each electric bus and gas node are fed into the artificial neural network as input neurons; optimal economic dispatch results are set as output neurons, where the dispatch results can be either continuous (e.g. power and gas output) or binary (e.g. scenario feasibility). In generating power and gas outputs, the slack generator method is proposed to further eliminate load mismatch. Case studies on an integrated Belgium 20-node gas/IEEE 24-bus power system show that, after the artificial neural network is properly trained, the data-driven economic dispatch method is 1 0 4 ∼ 1 0 5 times faster than model-driven piecewise linearization. It even outperforms second-order cone programming, a well-known convex relaxation technique to model natural gas systems, in terms of both the coupled system’s state recovery accuracy and computational efficiency. Furthermore, the data-driven method is applied to a multi-period dispatch problem to demonstrate its scalability.