Optimal Dispatch Model Research Articles

Adjustable robust interval economic dispatch of integrated electricity and district heating systems under wind power uncertainty

With the increasing capacity of wind power in power systems, wind power volatility and uncertainty pose a great threat to the operation of the electricity system, especially for areas with high heating demand. Integrated electricity and district heating system (IEDHS) is a promising option to improve power system flexibility to accommodate more wind power. This paper excavates the potentiality of coordinated operation of IEDHS to alleviate the effects of wind power uncertainty with an adjustable robust interval approach. Firstly, the structure of IEDHS and operation models of district heating network are presented. An adjustable robust interval optimal dispatch model for IEDHS is established considering wind power uncertainty. The wind power mismatches in the worst-case scenarios are balanced by adjustable thermal units based on the time-varying participation factors. Then, the nonlinear mathematical formulation of the scheduling model is proposed and converted into a mixed-integer linear programming (MILP) problem utilizing a binary expansion method. By solving this model, the maximum allowable output intervals of wind power and optimal economic dispatch results of IEDHS can be obtained. Finally, case studies performed on two IEDHS are applied to verify the validity of the proposed model.

Risk-averse TSO-DSOs coordinated distributed dispatching considering renewable energy and demand response uncertainties

The renewable energy generation (REG) integration has introduced a significant amount of uncertainties to power systems. This new trend requires power systems to have sufficient flexibility to mitigate the power imbalance between supply and demand. Demand response (DR), as a potential flexibility resource, has gained widespread attention in recent years. However, communication failures or customers’ unexpected behaviors may result in DR uncertainties, leading to operational risks. To cope with the impacts of DR and REG uncertainties, this work proposes a stochastic programming-based risk-averse optimal dispatch model to reduce both electricity purchasing cost and potential risk cost through transmission network (TN) and active distribution networks (ADNs) coordinations. The risk-averse distributed optimal dispatch problem is formulated as two sub optimization models. One conducts the transmission-level risk-averse generation dispatch, while the other represents the distribution-level ADN market clearings. Moreover, the conditional value at risk (CVaR) is used to measure the potential risk cost caused by DR and REG uncertainties. Finally, the optimization model is solved by a distributed solution algorithm, alternating direction method of multipliers (ADMM), to preserve data privacy between multiple stakeholders and reduce communication requirements. The performance of the proposed approach is evaluated in T30-D2 × 33 and T118-D85 + D2 × 69 test systems, the results validate its effectiveness.

Dispatch optimization of a concentrating solar power system under uncertain solar irradiance and energy prices

The integration of thermal energy storage into a concentrating solar power system allows for mitigating some of the risk associated with uncertain solar irradiance and uncertain energy prices. We solve a 48 h dispatch optimization model with continually updated conditional point forecasts of both direct normal irradiance (DNI) and electricity prices with a rolling-horizon scheme at hourly resolution over the course of a year. Joint, conditional forecasts for DNI and prices are formed using an autoregressive moving-average time series model with exogenous weather predictors. We guide dispatch using a mixed-integer programming model, but in order to evaluate performance we use the System Advisor Model (SAM) of the National Renewable Energy Laboratory. SAM is a techno-economic simulation model that accounts for plant thermodynamics with higher fidelity. Our conditional DNI forecasts improve annual revenue by 4%–12% over using historical forecasts based on data from previous years. Conditional price forecasts improve annual revenue by 6%–19% in the real-time market over analogous historical forecasts. Updating these forecasts every six hours, rather than every 24 h, further improves annual revenue by 5%–6%. We also investigate a method that values terminal inventory in our dispatch optimization model, again when used in a rolling-horizon scheme.

Optimal Dispatch of Multi-energy System Based on Carbon Trading Auxiliary Service

The energy economy, flexibility and low carbon of multi-energy system is one of the research hotspots of multi-energy system, a comprehensive energy system optimization scheduling method considering carbon trading is proposed. On the basis of considering the dynamic output of heat network equipment, an optimal dispatch model of the integrated energy system is constructed through energy coupling equipment such as cogeneration units, heat pumps, and gas turbines. Firstly, the thermal dynamics of heating pipes and heating buildings are modeled in detail. Then, taking the lowest operating cost of the integrated energy system and the lowest carbon transaction cost as the objective function, and taking the energy balance constraints and network constraints as the constraints, a day-ahead optimal scheduling scheme of the integrated energy system is proposed. Finally, the simulation results show that the multi-energy optimal scheduling method can effectively reduce the expenditure of the multi-energy system, increase the penetration ratio of wind and solar power generation, and reduce the overall carbon emissions of the system.

Optimal load dispatch of energy hub considering uncertainties of renewable energy and demand response

As an important modeling method for multi-energy systems, the energy hub (EH) plays an important role in the planning and operation research of multi-energy systems. In this regard, this paper proposes an optimal load dispatch model for the EH system by considering the coupling relationship of electric and thermal energy. The target of the proposed model is to reduce the total cost of the EH system, including resource cost, carbon emission cost, demand response (DR) cost, and system maintenance cost. An adjustable robust optimization method is presented to model the uncertainty of renewable energies (REs) and DR programs. Through duality theory, the proposed robust optimization problem is transformed into a mixed-integer linear programming problem and solved efficiently by a commercial solver. This paper carries out simulation experiments on three different cases with or without energy storage systems and DR programs. The simulation results show that the total cost of the EH system can be effectively reduced by installing energy storage systems and implementing DR programs. At the same time, the results also show that some economic benefits of the EH system need to be sacrificed to deal with the uncertainty of REs and DR programs.

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.

Robust optimal dispatch of distribution system considering carbon capture and demand response

Participation of carbon capture power plants and demand response in power system dispatch is an important mean to achieve the carbon-neutral goal. In order to take into account uncertainty of wind power and low-carbon economic operation of the distribution system, a two-stage robust optimal dispatch model of the distribution system considering carbon capture and demand response is proposed in this paper. In the day-ahead stage, the unit commitment plan and the price demand response (PDR) schedule are established. In the intra-day stage, the carbon related cost is included in the optimization objective. The strategies for the generators and the incentive demand response (IDR) are optimized under the worst-case output of wind power based on the results of the day-ahead stage. Karush-Kuhn-Tucker (KKT) conditions and the column-and-constraint generation (C&CG) algorithm are used to solve the proposed two-stage model. A revised IEEE 33-bus distribution system is used to verify the feasibility and effectiveness of the proposed model.

Research on Power System Dispatching Operation under High Proportion of Wind Power Consumption

For the multi-energy power system composed of thermal power, wind power, and a pumped-storage power station aiming at minimizing coal consumption of the power grid, an optimal dispatch model is established in this paper. Its advantage is to allow the power grid to accept a high proportion of new energy while ensuring power demand. The dynamic programming method is used to solve the problem. In the solution process, the traditional dynamic programming method is improved by introducing the penalty function and the dynamic value of the state variable, which can ensure the reliability of the power supply while achieving the optimization goal, as well as realize the full utilization of energy. Using the example of a high proportion of wind power systems with a pumped-storage power station as the energy storage mode and considering the relevant constraints after the heating transformation of the thermal power plant, our built model solves these challenges. The results show that when the maximum pumping power of the pumped-storage power station reaches 1138 MW and the maximum generating power reaches 755 MW, the wind curtailment and power rationing during the off-peak period of heating can be reduced from the previous 58,158 MWH and 46,838 MWH to almost 0, and the wind curtailment and power rationing during the peak period of heating can be reduced from the previous 77,656 MWH and 53,780 MWH to almost 0, so as to realize the flexible operation of the power grid.

Multi-Objective optimal scheduling of island microgrids considering the uncertainty of renewable energy output

The grid connection of wind power and photovoltaics significantly increases the uncertainty of the operation and scheduling of power systems. The maximum power that can be transmitted in a particular time slot often fluctuates randomly around a certain value according to a certain probability distribution, and a prediction error occurs between the predicted and actual power. When the actual maximum power that wind power and photovoltaics can generate is less than the power arranged in the scheduling plan, a significant increase in operating costs will occur, as well as an increase in the frequency fluctuation of the microgrid. This may harm the system. To study the effects of the uncertainty of renewable energy output on microgrid dispatching, this paper divides power sources into basic load and frequency modulated (FM) power sources according to the FM characteristics and establishes a multi-objective optimal dispatching model of island microgrids considering the uncertainty of renewable energy output, using the economic benefit and frequency fluctuation rate of the microgrids as the optimization target. Additionally, this paper proposes a two-step optimal scheduling method based on power classification: First, the Monte Carlo method is used to estimate the expectation and variance of the objective function, and a certain number of samples are obtained through sampling. Second, the particle swarm optimization algorithm is used to determine the installation plan of the FM and base load power sources to minimize the average sample cost. Finally, a backpropagation neural network is used to construct the frequency modulation strategy of FM power sources. An analysis with examples shows that, in the context of many samples, the model and proposed two-step scheduling method are reasonable and effective.

Analysis and Optimization of Urban Public Transport Lines Based on Multiobjective Adaptive Particle Swarm Optimization

Urban public transport is a very complex system, and with the development of urbanization, there are many new urban traffic characteristics. Making bus routes and scheduling strategies more efficient, scientific and accurate has a positive impact on the actual operation of public transport. To solve the urban public transport line design problem, this paper describes the implicit law of the characteristics of public transport travel from a deep perspective and analyzes the forms, influencing factors and existing problems of bus dispatching. By establishing a multiobjective public transport dispatching optimization model, starting from bus companies, passengers and government departments, public transportation operating costs comprehensively consider the interests of various parties and finally realize the optimization objective of minimizing fixed costs, fuel costs, carbon emission costs and time window penalty costs. The objective function is set reasonably, and the generation and optimization method of the initial line set in the public transport line design problem is improved; suitable constraint conditions and evaluation indicators are considered. This paper attempts to control the overall length of the bus line on the premise of fully meeting the travel needs of passengers. By solving the multiobjective problem on the same network and comparing different multiobjective optimization algorithms, the effectiveness of the method is evaluated. Additionally, an improved multiobjective adaptive particle swarm optimization (MOAPSO) is proposed, which has the characteristics of faster convergence, higher efficiency and low computational complexity. The simulation experimental results show that the proposed algorithm in this paper can obtain a better Pareto optimal solution set and can effectively solve the multiobjective model. The departure interval conforms to the passenger flow distribution, which can effectively reduce costs and improve the travel service quality of passengers on a large-scale network.

Optimization Model and Settlement Mechanism of Electricity Market in Zhejiang Province under the Form of Diversified and Highly Resilient Grid

Taking Zhejiang power market as the research object, this article compares the spot market, medium and long-term market and auxiliary service market models in Zhejiang Province with reference to the theories and practical experiences related to the construction of power market at home and abroad. Combining the situation of Zhejiang Province, the network situation and the current situation of power market construction, a multi-period generation dispatch optimization model based on power contract is proposed, and a day-ahead market clearing model adapted to distributed energy participation is established. Based on the deep learning algorithm using simulation cases of multiple scenarios, the power market model under the multi-fusion high resilience grid form is designed to provide suggestions for the design of generation dispatch optimization model and settlement mechanism for different power market development stages.

Collaborative optimal dispatch of microgrid and electric vehicles based on the Stackelberg game

The development of both microgrids and electric vehicles has become an important part of the current energy scenario. Useful complementary advantages can be formed between electric vehicles and microgrids, the consumers of which can utilize renewable energy and narrow the peak–valley differences of the net load curve while ensuring their own pecuniary interests. Based on the idea of the Stackelberg game, an optimal dispatch model of a microgrid with electric vehicles is proposed herein, where the benefits of the state of charge are taken into account. In the upper layer of the model, the charging and discharging behaviors of electric vehicles are guided by the goal of minimizing the operating cost of the microgrid. In the lower layer of the model, electric vehicle users adjust the charging and discharging strategies with the goal of maximizing their individual interests. The study results demonstrate that the proposed model not only reflects the benefits of both the master and slave but also reduces the peak–valley differences of the microgrid load. Further, the charging and discharging times of electric vehicles are reduced, and their state of charge is maintained at a high level.

An optimal dispatch model for virtual power plant that incorporates carbon trading and green certificate trading

The grid connection of large-scale clean energy provides the possibility for the establishment of a clean energy system. The urgent problem that needs to be solved is how to improve the utilization efficiency of clean energy to reduce carbon emissions. First, the virtual power plant (VPP) operation mode under the carbon trading and green certificate trading mechanism is analyzed. Secondly, this paper incorporates carbon trading mechanism and green certificate trading mechanism into the optimal dispatch model of VPP including wind power generation, photovoltaic power generation, gas turbines and energy storage device. Taking the net profit of VPP as the optimization goal, which can take into account both economic and environmental protection. Based on whether VPP participates in carbon trading and green certificate trading, three schemes are established and compared and analyzed. In addition, to cope with the volatility of renewable energy, the utilization rates of the renewable energy output scenarios for four typical days under the three schemes were compared and analyzed. To solve this problem, this paper proposes the Self-Conclusion and Variational Particle Swarm Optimization (SCV-PSO) algorithm. The simulation results show that the VPP optimal scheduling model and solving algorithm proposed can effectively improve the utilization rate of renewable energy and reduce the carbon emission under the premise of ensuring economic efficiency. It can provide a useful reference for the low-carbon economic operation of the power system in the future.

A Tri-dimensional Equilibrium-based stochastic optimal dispatching model for a novel virtual power plant incorporating carbon Capture, Power-to-Gas and electric vehicle aggregator

This study proposes a novel structure of carbon-to-power-based virtual power plant (C2P-VPP) considering the flexible demand response and electric vehicle-to-grid aggregators (EVA). C2P is integrated by gas-power plant carbon capture (GPPCC), carbon storage devices (CS), and power-to-gas (P2G). Then, to balance the multiple objectives of the dispatching cost, the carbon emission and the output fluctuation, a tri-dimensional coordinated optimal dispatching model is construct from the perspective of the energy impossibility triangle problem. And the robust optimization theory is applied to characterize the uncertainty of wind power plant (WPP) and photovoltaic power generation (PV). Thirdly, to solve the above multi-objective model, an improved fuzzy equilibrium coordination-based-model solution algorithm is proposed. The algorithm integrates the fuzzy satisfaction theory, the multi-objective input income table, the entropy weight method into the complete information static game model. Finally, the CIGRE medium voltage distribution system is chosen for case study, the results show: (1) GPPCC could capture and store CO2 in carbon storage devices, and P2G could convert the CO2 into CH4 for conventional gas turbines (CGT) providing flexible power output. Compared with the scenario only with CS or P2G, when they are both introduced, the carbon emissions reduced by 7.75 % and 3.82 %, the dispatching cost reduced by 2.90 % and 7.77 %, and the output fluctuation reduced by 10.03 % and 2.80 %. (2) When robust coefficient Г is from 0.85 to 0.95, the decision-maker is the risk preference type and willing to bear risk to pursue excess benefit. (3) Sensitivity analysis shows when the rated capacity ratio of CGT, GPPCC and P2G is from 8:4:1 to 2:2:1, the dispatching scheme are optimal. When the capacity ratio of WPP, PV and EVA is from 20:1 to 10:1, the objective value changes significantly, and the optimal equilibrium scheme could be established when the capacity ratio is higher than 10:1. Therefore, the proposed optimal dispatching model could provide an effective tools for decision makers to achieve the optimal dispatching schemes.

Optimal Dispatching of Ladder-Type Carbon Trading in Integrated Energy System With Advanced Adiabatic Compressed Air Energy Storage

Advanced adiabatic compressed air energy storage (AA-CAES) is a promising form of CAES technology, which can realize multi-energy cascade storage and supply. Given the characteristics of cooling, heating, and power (CHP) load demand of regionally integrated energy system (IES) and the overall need to control the total carbon emission, this study first establishes the AA-CAES multi-energy storage model; second, based on analyzing the multi-energy characteristics of AA-CAES, a ladder-type carbon trading mechanism is introduced; furthermore, aiming for minimal system operating costs and carbon trading costs, an optimal dispatch model of an integrated energy system with AA-CAES as the energy hub for coupling multiple energy forms is established. Finally, an empirical study was conducted based on the energy use scenario of a provincial and ministerial university in Qinghai Province. This study analyzes the impact of the operation of AA-CAES and the introduction of ladder-type carbon trading on the operating costs and carbon trading costs of IES. The simulation results show that the total operating cost of the proposed model is reduced by 23.9%, and carbon emission is decreased by 14.5% compared to the conventional IES. It proves the validity of coupling AA-CAES and stepped carbon trading mechanisms to facilitate low-carbon economy in IES.

The Impact of Wind Generation on Operating Cost and Profit in a Bilevel Optimization Dispatch Model

In this paper, the problem of economic dispatch of energy of an electrical system is solved, based on three different approaches: first is deterministic, a second is stochastic and a third given by bilevel programming. It is observed that when solving the deterministic economic energy dispatch, results follow the principle of order of merit, likewise under some scenarios availability of a large flexible capacity during the power balance stage becomes necessary. In another hand, the stochastic model becomes a tool that optimizes flexible capacity by sacrificing the order of merit which is not the case of bilevel model that adopts both, order of merit and optimization of flexible capacity by choosing an optimum of wind power to dispatch. In terms of profit, it has been observed that under the stochastic approach those flexible participants must fall into losses under certain scenarios. However, under the bilevel approach, the aforementioned issue is improved. Finally and in order to illustrate the impact of the bilevel approach a 24-node system serves as a test system to obtain results and demonstrate the economic advantages of an bilevel dispatch.

Dynamic dispatch of connected taxis for large-scale urban road networks with stochastic demands: An MFD-enabled hierarchical and cooperative approach

The dynamic dispatch of taxis is an important way to improve the service efficiency of taxis and reduce cruising distance. However, in a complex urban road network with extensive and stochastic roadside service demands, current macroscopic dispatch methods can hardly establish accurate and real-time matching relationships between taxis and passengers, and microscopic dispatch methods have the disadvantage of excessive computational complexity and nonupdatable match relationships. To this end, this paper proposes a hierarchical and cooperative macroscopic and microscopic dynamic dispatching approach for real-time urban network taxis in a connected taxi (CTaxi) information environment. First, network traffic flow dynamic evolution is described based on a macroscopic fundamental diagram (MFD), and the optimal macroscopic dispatching model is proposed to optimize the distribution of CTaxis for large-scale urban road networks with multiple MFD sub-regions. Second, to avoid the phenomenon of CTaxis blindly searching for passengers, a microscopic and relationship-updatable dispatching strategy between CTaxis and passengers is established within each MFD sub-region considering stochastic service demands and the signal timing of intersections. Finally, numerical experiments are conducted in a real road network to compare the performance of four methods: no CTaxi dispatch (NCD), conventional CTaxi dispatch (CCD), MPC dispatching (MPCD) and the dispatching strategy cooperating macro and micro (DSCMM) proposed in this paper. The results show that the DSCMM method can reduce the idle driving distance by 26.8%, 10.8% and 8.3% and the passenger waiting time by 11.0%, 13.4% and 6.4% compared to NCD, CCD and MPCD, respectively. Additionally, the market allocation of CTaxis under four scenarios is discussed, and the results demonstrate that the proposed DSCMM method is effective in improving the efficiency of CTaxis operation and reducing the waste of service resources in urban road networks with different service scales.

Optimization Model of Electric Vehicles Charging and Discharging Strategy Considering the Safe Operation of Distribution Network

Against the background of carbon neutrality, the power dispatching operation mode has undergone great changes. It not only gradually realizes the coordinated control of source–grid–load–storage, but also strives to realize the multi-level coordination of the transmission network, distribution network and microgrid. Disorderly charging and discharging of large-scale electric vehicles (EVs) will have a great negative impact on the distribution network, but aggregating EVs and guiding them to charge and discharge in an orderly manner will play a positive role in delaying investment in the distribution network. Therefore, it is urgent to adopt an effective scheduling control strategy for electric vehicle charging and discharging. First, a variety of indexes were set to analyze the influence of EVs access on distribution network and the correlation between the indexes. Then, by defining the EVs penetration rate and the load simultaneous rate, the charging load planning of EVs was calculated. Based on the simultaneous load rate, the regional electricity load plan was calculated, and a configuration model of distribution capacity suitable for charging loads in different regions was constructed. Finally, an optimal dispatch model for electric vehicles considering the safety of distribution network was proposed and the distribution transformer capacity allocation model was used as the optimization target constraint. Compared with most optimized dispatch models used to maximize aggregator revenues and reduce peak-to-valley differences and load fluctuations in distribution networks, this model could effectively reduce unnecessary investment while meeting regional distribution transformer needs and maintaining distribution network security. Taking the improved IEEE 34-bus systems as an example, the simulation analysis was carried out and the investment demand of distribution network under the condition of disordered and orderly charge and discharge was compared. The results show that the proposed optimal scheduling method can effectively reduce the load fluctuation of distribution network, keep the voltage offset within the allowable voltage deviation range, and can effectively delay the investment of distribution network.

Two-Stage Robust Optimal Scheduling of “Nearly-Zero-Carbon Park” Considering the Synergy of Waste Disposal and Carbon Capture Gas-Fired Power Plants

Aiming at the demand characteristics of “Nearly-zero Carbon Parks" (NZCP) powered by all renewable energy for low-carbon emissions and waste reduction, a NZCP topology and a two-stage robust optimal scheduling model based on the Carbon Capture Gas-fired Power Plant (CCGPP)-Power to Gas(P2G)-Waste Disposal (WD) collaborative model are proposed in this paper. First, the relationship between WD, CCGPP and energy supply and demand that can be generated from renewable energy is studied, and according to the system energy regulation requirements, the energy flexibility of residential loads, small industrial loads and electric vehicles are considered, and the NZCP mathematical model based on the CCGPP-P2G-WD collaborative model is established. Secondly, NZCP energy supply and demand characteristics are studied, and an energy coordination model for NZCP energy conversion and storage powered by 100% renewable energy is established. Then, wind power, photovoltaic power, and combined demand response uncertainties are considered, and the NZCP two-stage robust hybrid optimal dispatch model is established based on second-order cone relaxation and dual theory. Finally, the simulation analysis is carried out based on the operation data of the integrated energy system of a park in a certain area in northern my country. The simulation results show that the proposed two-stage robust optimal scheduling model of NZCP coordinated with WD and CCGPP can effectively improve the economy of the system at a higher energy balance level, improve the absorption capacity of renewable energy, and provide better multi-time scale energy regulation characteristics for NZCP with a larger energy supply scale.

Optimal dispatch model for PV-electrolysis plants in self-consumption regime to produce green hydrogen: A Spanish case study

The production of green hydrogen from renewable energy by means of water electrolysis is a promising approach to support energy sector decarbonization. This paper presents a techno-economic model of plants with PV sources connected to electrolysis in self-consumption regime that considers the dynamics of electrolysis systems. The model calculates the optimal hourly dispatch of the electrolysis system including the operational states (production, standby, and idle), the load factor in production, and the energy imports and exports to the electricity grid. Results indicate that the model is a useful decision support tool to operate electrolysis plants connected to PV plants in self-consumption regimes with the target of reducing hydrogen production costs.