Economic Dispatch Strategy Research Articles

Distributed dynamic economic dispatch of biogas-wind-solar-hydrogen multi-microgrid system considering individual selfishness

This paper proposes a biogas-wind-solar-hydrogen multi-microgrid system to address the issues of poor economy and reliability, as well as the waste of wind and solar energy, in single energy-based isolated microgrid systems. The study considers the coupling constraints of multiple time scales and establishes a dynamic economic dispatch model. Furthermore, a consensus-based distributed dynamic economic dispatch strategy is proposed. To tackle the challenge of unified economic dispatch caused by the interaction among multiple microgrids in the joint operation of the biogas-wind-solar-hydrogen multi-microgrid system, a microgrid selfishness impact model and elimination strategy are developed. Simulation results demonstrate the effectiveness and superiority of the proposed distributed dynamic economic dispatch strategy considering individual selfishness in the biogas-wind-solar-hydrogen multi-microgrid system.

Research on Multi-Microgrid Electricity–Carbon Collaborative Sharing and Benefit Allocation Based on Emergy Value and Carbon Trading

In response to climate change, the proportion of renewable energy penetration is increasing daily. However, there is a lack of flexible energy transfer mechanisms. The optimization effect of low-carbon economic dispatch in a single park is limited. In the context of the sharing economy, this study proposes a research method for multi-park electricity sharing and benefit allocation based on carbon credit trading. Firstly, a framework for multi-park system operation is constructed, and an energy hub model is established for the electrical, cooling, and heating interconnections with various energy conversions. Secondly, a park carbon emission reduction trading model is established based on the carbon credit mechanism, further forming an optimal economic and environmental dispatch strategy for multi-park electricity sharing. Matlab+Gurobi is used for solving. Then, based on asymmetric Nash bargaining, the comprehensive contribution rate of each park is calculated by considering their energy contribution and carbon emission reduction contribution, thereby achieving a fair distribution of cooperation benefits among multiple parks. The results show that the proposed method can effectively reduce the overall operational cost of multiple parks and decrease carbon emissions, and the benefit allocation strategy used is fair and reasonable, effectively motivating the construction of new energy in parks and encouraging active participation in cooperative operations by all parks.

Economic Scheduling Strategy for Multi-Energy-Integrated Highway Service Centers Considering Carbon Trading and Critical Peak Pricing Mechanism

This study proposes an optimized economic scheduling strategy for multi-energy-integrated highway service centers (MEIHSCs) within a 24 h operational timeframe. With the imperative of carbon peaking and carbon neutrality, highway areas are increasingly incorporating renewable energy systems, such as photovoltaic arrays, to capitalize on abundant resources along highways. Considering the diverse load demands of new energy vehicles and the mismatch between energy supply and demand on the highway, MEIHSCs must adapt to these trends by establishing integrated networks for electricity, natural gas, and hydrogen refueling. However, there is a lack of coordination between equipment switching and the phases of low electricity prices and peak renewable energy periods. To address this challenge and improve economic efficiency, this study proposes an economic dispatch strategy that combines economic incentives based on carbon trading and critical peak pricing mechanisms. This strategy aims to maximize economic benefits while fully meeting the load demands of new energy vehicles. Case studies indicate that operating costs are reduced by 28.04% compared to strategies without new energy installations, and by 47.85% compared to strategies without optimization. The results demonstrate that this integrated and optimized strategy significantly reduces energy costs and enhances economic benefits in highway service centers.

Distributed predefined-time optimal economic dispatch for microgrids

With the massive popularization of distributed generators, optimal economic dispatch has been a key optimization problem to maintain stable and efficient work of the whole system. In this paper, a new smooth reconstruction penalty function with continuous and piecewise linear differential is designed to deal with generation power constraints, which promotes to obtain a better suboptimal solution compared with the existing smooth penalty methods. A distributed predefined-time optimal economic dispatch strategy is presented by utilizing a time-based function. By employing the proposed strategy, the minimization of the generation cost with transmission loss under the power balance constraint and generation minimum/maximum constraints can be realized within a predefined settling time. The performance of the proposed optimization strategy is evaluated by simulations and hardware-in-the-loop experiments in terms of validity verification, robustness to load change and topology reconfiguration, plug-and-play functionality, and comparison with the existing results to illustrate the advantages of fast convergence and near optimal results.

Synergizing renewable energy sources in building-integrated hybrid energy systems via niche-ant colony optimization

The rapid advancement of Building-Integrated Hybrid Energy Systems (BIHES), characterized by renewable energy sources with variable generation patterns, presents significant challenges for effective integration and utilization. This paper introduces an optimal economic dispatch strategy for commercial BIHES using a Niche-Ant Colony Optimization (NACO) Algorithm. BIHES integrates renewable sources such as 200 kW photovoltaic panels, 150 kW wind turbines, and 500 kWh energy storage systems within commercial buildings. A detailed mathematical model for economic dispatch leverages a multi-load demand of 1.2 MW and flexible adjustment capabilities. The model aims to maximize on-site renewable energy use while balancing grid interaction. The NACO Algorithm, tested through comprehensive simulations, demonstrated significant improvements, resulting in a 12.96 % reduction in environmental costs, an 18.4 % reduction in the Levelized Cost of Energy (LCOE), and a 14.25 % reduction in total operational costs compared to traditional methods. Additionally, the system achieved annual CO2 savings of 25,000 tons, increased renewable energy absorption by 22 %, reduced grid dependency by 30 %, and decreased energy purchase costs by 20 %. Energy storage optimization also extended the storage system's lifecycle by 15 %. These results highlight the enhanced synergy and complementarity of multiple energy sources within BIHES, showcasing its potential for substantial economic and environmental benefits.

Low-carbon economic dispatch strategy for integrated electrical and gas system with GCCP based on multi-agent deep reinforcement learning

Low-carbon economic dispatch strategy for integrated electrical and gas system with GCCP based on multi-agent deep reinforcement learning

Low-carbon economic dispatch strategy for microgrids considering stepwise carbon trading and generalized energy storage

Integrating carbon trading mechanisms with generalized energy storage (GES) fully embodies the principles of green and coordinated development, serving as a crucial means to achieve low-carbon construction of microgrids. This research presents a strategy for optimizing energy allocation within microgrids to minimize carbon emissions and enhance microgrid systems' economic-environmental benefits. The strategy takes into account the use of tiered carbon trading and GES. Based on a typical microgrid system architecture, an economic dispatch model for microgrids is developed, which integrates renewable energy sources such as wind and solar storage, gas turbines, energy storage systems, and flexible resources on the demand side. The model aims to minimize carbon emissions while optimizing the allocation of resources. Subsequently, the model facilitates microgrid carbon emission control by considering the transferable, convertible, and reducible properties of GES. Furthermore, implementing a tiered carbon trading mechanism decreases carbon emissions. Finally, using a real microgrid example from a specific region in China, the results indicate that the proposed method significantly enhances the system's low-carbon level. Notably, compared to scenarios that do not consider GES, the proposed method substantially reduces total costs by 6.62% and decreases carbon emissions by 22.2%. The findings indicate that the suggested dispatch model can substantially decrease carbon emissions while simultaneously improving the economic efficiency of the microgrid system.

Multi-time scale economic regulation model of virtual power plant considering multiple uncertainties of source, load and storag

A novel multi-stage time scale economic dispatch scheme is proposed for virtual power plants, taking into account the uncertainties arising from the connection of distribution network sources. This research introduces specific scheduling schemes tailored to various time scales within distribution networks, including a fuzzy optimized day ahead scheduling scheme, an intra-day scheduling scheme combined with Deep Q Network, and an adaptive optimized real-time scheduling scheme. This plan mainly considers the impact of photovoltaic output and conducts scheduling one day in advance through fuzzy optimization. In the intraday scheduling, different strategies were adopted in the study. By combining with Deep Q Network, research on scheduling for intraday demand within the power system. The analysis is conducted through rigorous modeling. Experimental tests were conducted to evaluate the performance of the proposed schemes. The day ahead dispatching primarily considers the impact of photovoltaic output and calculates the cost associated with each link in the grid under three different meteorological conditions. In the intra-day scheduling, the total costs for Scenario 1, Scenario 2, and Scenario 3 are found to be 34,724.5 yuan, 36,296.5 yuan, and 33,275.8 yuan, respectively. Notably, strategies 1 and 2 demonstrate lower costs compared to the pre-day scheduling, with the exception of Scenario 3. In real-time scheduling, considering the matching between sources and sources, the matching rate between sources and sources can be maintained at over 95%, and the stability and cost of the power grid have significantly decreased. In summary, by proposing a multi-stage time scale economic scheduling scheme, this study fully considers the uncertainty of the power supply of the distribution network access, as well as the different needs of day, day and real-time scheduling, providing an effective solution for the power dispatching of virtual power plants and providing important technical support for the reliability and economy of the power system.

Quantification of Uncertainty Cost Functions for Controllable Solar Power Modeling

Navigating the secheduling of generation resources of energy in power systems marked by a significant presence of renewable generation involves intricate optimization challenges. The conventional tools for resolving such challenges include programming techniques and heuristic approaches, both contingent upon a precisely articulated target function for optimization. Traditional optimization tools rely on precisely defined target functions, but the evolving landscape of power systems introduces complexity, especially with unpredictable behaviors of renewable sources. The research specifically quantifies penalty costs associated with photovoltaic (PV) generators, employing probabilistic methods for a robust mathematical analysis. The developed analytical model enhances adaptability in economic dispatch problems, considering uncertainty in decision-making. Validation using Monte Carlo simulation emphasizes uncertainty in PV generation and highlights the advantages of the proposed analytic model. The quadratic form of the model aligns coherently with simulation outcomes, contributing significantly to understanding uncertainty quantification in solar power modeling. The research aims to refine controllable solar power models, establish robust uncertainty cost functions, and improve the accuracy of economic dispatch strategies. Ultimately, this work promotes the seamless integration of solar energy into diverse and dynamic energy grids.

Dynamic vulnerability assessment of hybrid system considering wind power uncertainty

The stochastic volatility of wind power introduces numerous uncertainties to the security and stability of the hybrid system, which in turn affects the overall vulnerability level of the system. In this study, a wind power output model incorporating the effects of wind abandonment and uncertainty factors is established. Additionally, a hybrid optimal power flow (OPF) method considering economic dispatch, stochastic trip, and islanding strategies is developed to simulate the power flow dispatch during cascading failures. Furthermore, a set of indexes describing the global dynamic vulnerability of the hybrid system is proposed to reveal the effects of changes in wind power uncertainty and penetration levels on the hybrid system in the short term. Simulations were conducted on an adapted IEEE118 system, and the results demonstrate that an increase in wind power uncertainty and penetration will exacerbate the system’s vulnerability. This severity is particularly pronounced during peak and valley load periods, making the hybrid system more susceptible to large-scale outages. When the wind power uncertainty level grows to 4, the overall vulnerability level of the hybrid system increases by 102.3% compared to the initial uncertainty case. When the wind penetration level reaches 46%, the overall vulnerability level rises by 75.9% compared to the initial system. These findings emphasize the sensitivity of the hybrid system to different levels of wind power uncertainties and penetrations, providing evidence-based support for the prevention of system cascade failures.

Environmental and economic dispatching strategy for power system with the complementary combination of wind-solar-hydro-thermal-storage multiple sources

The linkage, coordination, and complementary cooperation of energy supply can improve the efficiency of transportation and utilization. At present, the level of new energy consumption needs to be improved, the coordination of the source network load storage link is insufficient, and the insufficient complementarity of various types of power sources in the power system. This article fully explores the differences and complementarities of various types of wind-solar-hydro-thermal-storage power sources, a hierarchical environmental and economic dispatch model for the power system has been established. Among them, the upper level model takes the flexible consumption of new energy as the optimization goal, the middle level model uses a combination of hydropower station and energy storage to minimizes the fluctuation variance and peak-to-valley difference of the net load curve, the lower level model aims to achieve optimal environmental and economic benefits of the power system, comprehensively considers the coal consumption cost, startup and shutdown cost, energy storage operation cost, and pollutant emissions of thermal power units, determines the startup and shutdown mode and output power of thermal power units. Finally, an improved IEEE 6-machine 30-node system is used as an example for simulation analysis, the results show that after applying the proposed hierarchical environmental and economic dispatch strategy of the power system, the fluctuation variance and the peak-to-valley difference of the net load curve have been reduced by 46.3% and 31.5%, respectively, and the environmental and economic benefits of the system is improved by 5.1% compared with the traditional economic dispatch strategy. It can meet the requirements of energy system cleaning and decarbonization while improving the operation economy, which verifies the effectiveness of the proposed environmental economic dispatch model.

Low-carbon economic dispatch strategy for integrated power system based on the substitution effect of carbon tax and carbon trading

As the introduction of carbon peak and carbon neutrality, the intensity of carbon regulation is growing. The power system, as the main body of carbon emission, will face the constraints of compound carbon reduction policy. To address this issue, a low-carbon economic dispatch strategy for integrated power system based on the substitution effect of carbon tax and carbon trading is proposed to improve the economic and environmental joint benefits. Firstly, facing the volatility characteristics of carbon trading price, Monte Carlo method is implemented to analyze the uncertainty of carbon trading price. Next, the renewable energy power and electricity load are forecast by long short-term memory method. Subsequently, the gray wolves algorithm is imposed to optimize the dispatch model and obtain the optimal dispatch plan. The comparative analysis reveals that the carbon emission and dynamic payback period of the optimized integrated power system are 4.37 × 106 t and 10.06 years, which are reduced 1.67 × 105 t and 0.3 years compared with the conventional power system. Besides, the economic analysis demonstrates that the substitution effect can decrease the unit cost of carbon emission by 1.03 USD/t. Finally, the comparative analysis manifests that the proposed dispatch model can effectively boost the economic and environmental joint benefits for the integrated power system.

Bi-level economic dispatch strategy for virtual power plants based on electric vehicles aggregation

Bi-level economic dispatch strategy for virtual power plants based on electric vehicles aggregation

Low-carbon optimal operation of the integrated energy system considering integrated demand response

In order to further reduce the carbon emission level of the integrated energy system and improve the system operation economy, a low-carbon economic dispatch strategy for the integrated energy system considering comprehensive demand response is proposed. First, based on the composition and operating characteristics of the integrated energy system, two demand response models, price-based and substitution-based, are established. Secondly, based on the ladder-type carbon trading mechanism and the green certificate trading mechanism, a green certificate-carbon trading mechanism model is designed to construct a ladder-type carbon trading mechanism model. A comprehensive energy system low-carbon economic dispatch model with the lowest system operating cost is composed of carbon transaction costs, energy purchase costs, operation and maintenance costs, green certificate trading income and wind and light abandonment costs. Finally, through simulation experiments, this paper is verified the effectiveness of the proposed model is analyzed, and the impact of demand response sensitivity and ladder-type carbon trading mechanism parameters on system dispatch results is analyzed.

Source-Load Coordinated Low-Carbon Economic Dispatch of Microgrid including Electric Vehicles

As the global warming crisis becomes increasingly serious, sustainable dispatch strategies that can reduce CO2 emissions are gradually developed. Aiming at the problems of poor synergy between carbon capture systems (CCS) and P2G as well as the potential of the source-load interaction of microgrids with electric vehicles for carbon reduction that needs to be explored, this paper proposes a source-load coordinated low-carbon economic dispatch strategy for microgrids, including electric vehicles. Firstly, considering the low-carbon operation characteristics of CCS and P2G, a comprehensively flexible and cooperative operation mode for CCS and P2G is constructed. Secondly, based on the carbon reduction potential of demand response on the energy consumption side, a demand response optimal scheduling model considering the participation of electric vehicles in the microgrid is established. Finally, based on the complementary characteristics of low-carbon resources on both sides of the microgrid, a source-load coordinated low-carbon economic dispatch strategy for the microgrid is proposed. The results show that the strategy proposed in this paper can fully use the energy time-shift advantage of CCS and P2G and can combine EVs and other load-side resources to flexibly participate in demand-side response, which effectively realizes source-load synergy and improves the low carbon and economy of the microgrid.

Distributed low-carbon economic dispatch of integrated power and transportation system

Electric vehicles (EVs) are widely recognized as promising solutions to reduce carbon emissions from the transportation sector. The rapid proliferation of EVs intensifies the interdependency between the power distribution system (PDS) and transportation system (TS). This paper proposes a distributed low-carbon economic dispatch scheme for the integrated power and transportation system (IPTS). Carbon emissions from the TS are quantified by a carbon dioxide calculation model, and the emissions from the PDS are determined by the electricity generation of fossil-fueled generators and the carbon reduction of carbon capture and storage (CCS) devices. An integrated model reformulation method is employed to transform the original non-convex model into a tractable one. Then a distributed solution method based on the spectral penalty parameter based adaptive alternating direction method of multipliers (SPPA-ADMM) is developed to protect the privacy of the PDS and TS. The reformulated model is solved iteratively, and the penalty parameter of the SPPA-ADMM is adjusted adaptively to improve the convergence performance of the method. Numerical results on three test systems demonstrate the effectiveness of the proposed scheme in achieving low-carbon economic dispatch of IPTS. Moreover, case studies validate the superiority of the SPPA-ADMM based distributed solution method in computation performance.

Low-carbon economic dispatch strategy for interconnected multi-energy microgrids considering carbon emission accounting and profit allocation

Peer-to-peer energy trading among multi-energy microgrids is a promising pathway to improve energy efficiency, reduce carbon emissions, combat climate change, and promote sustainable development. This study proposes a multi-energy trading strategy for multi-energy microgrids that considers carbon tax policies to boost the low-carbon management of multi-energy microgrid clusters. First, a carbon emission accounting model with energy-carbon coupling is constructed to clarify the carbon emission transfer generated by energy transactions among multiple agents and to achieve accurate accounting of carbon emissions from each multi-energy microgrid. Second, the Nash-Harsanyi bargaining game theory is used to establish an energy trading alliance for multi-energy microgrid clusters. A profit allocation mechanism is designed to reflect the marginal contribution of each player to the economic and environmental effectiveness of the alliance. Finally, different carbon emission accounting methods and profit allocation mechanisms are compared. The simulation results demonstrate the effectiveness of the carbon emissions accounting model and the proposed energy trading strategy. The low-carbon scheduling framework among the multi-energy microgrids generated an economic gain of $ 1376.44 and 12.60 t carbon savings.

Economic Power Dispatch of a Grid-Tied Photovoltaic-Based Energy Management System: Co-Optimization Approach

The requirement for the integration of power plants due to the cyclical rise in electrical energy consumption is due to the fluctuating load demand experienced with the current grid systems. This integration necessitates effectively allocating loads to the power plants for a minimum grid-tied transmission line cost, while meeting the network constraints. In this paper, we formulate an optimization problem of minimizing the total operational cost of all committed plants transmitted to the grid, while also meeting the network constraints and ensuring economic power dispatch (EPD) and energy management system co-optimization. The developed particle swarm optimization (PSO) method resolves the optimization problem using a piecewise quadratic function to describe the operational cost of the generation units, and the B coefficient approach is employed to estimate the transmission losses. Intelligent adjustments are made to the acceleration coefficients, and a brand-new algorithm is suggested for distributing the initial power values to the generation units. The developed economic power dispatch strategy successfully demonstrated an imperative cost reduction, with a connected load of 850 MW, 1263 MW, and 2630 MW of power demand, contrasted with previous PSO application cost values percentage, maximum yearly cost savings of (0.55%, 91.87), (46.55%, 3.78), and (73.86%, 89.10), respectively, and significant environmental benefits. The proposed co-optimization approach can significantly enhance the self-consumption ratio compared to the baseline method.

Comparison amongst Lagrange, Firefly, and ABC Algorithms for Low-Noise Economic Dispatch and Reactive Power Compensation in Islanded Microgrids

For the microgrids to operate securely, distributed generators must be able to interact with one another without experiencing any communication delays or noise. To develop a more effective economic dispatch strategy, this research focuses on noise’s effect on the performance of an islanded microgrid. Three different strategies, Lagrange, firefly, and artificial bee colony algorithms, are compared for optimal solutions of economic dispatch. Their performance is compared based on stability during noise interference and faster response time with and without the virtual synchronous generator-STATCOM strategy. The virtual synchronous generator is used as a voltage source to regulate active power and reactive power with the grid. A STATCOM controller is introduced in the system for reactive power compensation. Reactive power compensation is the process of controlling reactive power to enhance the efficiency of alternating current power systems. By boosting the active power, reactive power compensation in the transmission system will increase the stability of microgrids. The voltage, output power, power factor, and phase angle of the microgrid benefit from the stability provided by the controller. As a result, the performance and resilience of the microgrid is improved.

CSP-IES economic dispatch strategy with generalized energy storage and a conditional value-at-risk model

To promote the efficient use of energy storage and renewable energy consumption in the integrated energy system (IES), an economic dispatch strategy for the concentrating solar power (CSP)-IES with generalized energy storage and a conditional value-at-risk (CVaR) model is proposed. First, considering the characteristics of energy storage and distributed power supply timing, a CSP-IES configuration is established by using a CSP plant to achieve thermal decoupling of the combined heat and power unit and by defining the thermal storage system of the CSP plant and the battery as the actual energy storage. Second, the fuzzy response of the logistic function is used to optimize the time-of-use tariff to guide load shifting, and the load shifting is defined as virtual energy storage. Third, the CSP-IES economic dispatch model is established to consider the carbon emission allowance model. Finally, considering the system uncertainty, a fuzzy chance constraint is used to relax the system power balance constraint, and then the trapezoidal fuzzy number is transformed into a deterministic equivalence class, and the CVaR model is used as a risk assessment index to quantify the risk cost of the system due to uncertainty. The CSP-IES economic dispatch model with CVaR is constructed. The feasibility and effectiveness of the proposed optimization model are verified by comparing various scenarios.