Economic Power Dispatching from Distributed Generations: Review of Optimization Techniques

In the increasingly decentralized energy environment,economical power dispatching from distributed generations(DGs)is crucial to minimizing operating costs,optimizing resource utilization,and guaranteeing a consistent and sustainable supply of electricity.A comprehensive review of optimization techniques for economic power dispatching from distributed generations is imperative to identify the most effective strategies for minimizing operational costs while maintaining grid stability and sustainability.The choice of optimization technique for economic power dispatching from DGs depends on a number of factors,such as the size and complexity of the power system,the availability of computational resources,and the specific requirements of the application.Optimization techniques for economic power dispatching from distributed generations(DGs)can be classified into two main categories:(i)Classical optimization techniques,(ii)Heuristic optimization techniques.In classical optimization techniques,the linear programming(LP)model is one of the most popular optimization methods.Utilizing the LP model,power demand and network constraints are met while minimizing the overall cost of generating electricity from DGs.This approach is efficient in determining the best DGs dispatch and is capable of handling challenging optimization issues in the large-scale system including renewables.The quadratic programming(QP)model,a classical optimization technique,is a further popular optimization method,to consider non-linearity.The QP model can take into account the quadratic cost of energy production,with consideration constraints like network capacity,voltage,and frequency.The metaheuristic optimization techniques are also used for economic power dispatching from DGs,which include genetic algorithms(GA),particle swarm optimization(PSO),and ant colony optimization(ACO).Also,Some researchers are developing hybrid optimization techniques that combine elements of classical and heuristic optimization techniques with the incorporation of droop control,predictive control,and fuzzy-based methods.These methods can deal with large-scale systems with many objectives and non-linear,non-convex optimization issues.The most popular approaches are the LP and QP models,while more difficult problems are handled using metaheuristic optimization techniques.In summary,in order to increase efficiency,reduce costs,and ensure a consistent supply of electricity,optimization techniques are essential tools used in economic power dispatching from DGs.

Two-Stage Optimal Dispatching of Wind Power-Photovoltaic-Solar Thermal Combined System Considering Economic Optimality and Fairness

Aiming at the problems of large-scale wind and solar grid connection,how to ensure the economy of system operation and how to realize fair scheduling between new energy power stations,a two-stage optimal dispatching model of wind power-photovoltaic-solar thermal combined system considering economic optimality and fairness is proposed.Firstly,the first stage dispatching model takes the overall economy optimization of the system as the goal and the principle of maximizing the consumption of wind and solar output,obtains the optimal output value under the economic conditions of each new energy station,and then obtains the maximum consumption space of the new energy station.Secondly,based on the optimization results of the first stage,the second stage dispatching model uses the dispatching method of fuzzy comprehensive ranking priority to prioritize the new energy stations,and then makes a fair allocation to the dispatching of the wind and solar stations.Finally,the analysis of a specific example shows that themodel can take into account the fairness of active power distribution of new energy stations on the basis of ensuring the economy of system operation,make full use of the consumption space,and realize the medium and long-term fairness distribution of dispatching plan.

A low-carbon economic dispatch model for electricity market with wind power based on improved ant-lion optimisation algorithm

Introducing carbon trading into electricity market can convert carbon dioxide into schedulable resources with economic value.However,the randomness of wind power generation puts forward higher requirements for electricity market transactions.Therefore,the carbon trading market is introduced into the wind power market,and a new form of low-carbon economic dispatch model is developed.First,the economic dispatch goal of wind power is be considered.It is projected to save money and reduce the cost of power generation for the system.The model includes risk operating costs to account for the impact of wind power output variability on the system,as well as wind farm negative efficiency operating costs to account for the loss caused by wind abandonment.The model also employs carbon trading market metrics to achieve the goal of lowering system carbon emissions,and analyze the impact of different carbon trading prices on the system.A low-carbon economic dispatch model for the wind power market is implemented based on the following two goals.Finally,the solution is optimised using the Ant-lion optimisation method,which combines Levi's flight mechanism and golden sine.The proposed model and algorithm's rationality is proven through the use of cases.

Economic Dispatch of Electrical Power System Based on the Multi-objective Co-evolutionary Algorithm

It is important to distribute the load efficiently to minimize the cost of the economic dispatch of electrical power system. The uncertainty and volatility of wind energy make the economic dispatch much more complex when the general power systems are combined with wind farms. The short term wind power prediction method was discussed in this paper. The method was based on the empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD). Furthermore,the effect of wind farms on the traditional economic dispatch of electrical power system was analyzed. The mathematical model of the economic dispatch was established considering the environmental factors and extra spinning reserve cost. The multi-objective co-evolutionary algorithm was used to figure out the model. And the results were compared with the NSGA-Ⅱ(non-dominated sorting genetic algorithm-Ⅱ) to verify its feasibility.

Scenario-oriented hybrid particle swarm optimization algorithm for robust economic dispatch of power system with wind power

An economic dispatch problem for power system with wind power is discussed.Using discrete scenario to describe uncertain wind powers,a threshold is given to identify bad scenario set.The bad-scenario-set robust economic dispatch model is established to minimize the total penalties on bad scenarios.A specialized hybrid particle swarm optimization(PSO)algorithm is developed through hybridizing simulated annealing(SA)operators.The SA operators are performed according to a scenario-oriented adaptive search rule in a neighborhood which is constructed based on the unit commitment constraints.Finally,an experiment is conducted.The computational results show that the developed algorithm outperforms the existing algorithms.

Low Carbon Economic Dispatch of Integrated Energy System Considering Power Supply Reliability and Integrated Demand Response

Integrated energy system optimization scheduling can improve energy efficiency and low carbon economy.This paper studies an electric-gas-heat integrated energy system,including the carbon capture system,energy coupling equipment,and renewable energy.An energy scheduling strategy based on deep reinforcement learning is proposed to minimize operation cost,carbon emission and enhance the power supply reliability.Firstly,the lowcarbon mathematical model of combined thermal and power unit,carbon capture system and power to gas unit(CCP)is established.Subsequently,we establish a low carbon multi-objective optimization model considering system operation cost,carbon emissions cost,integrated demand response,wind and photovoltaic curtailment,and load shedding costs.Furthermore,considering the intermittency of wind power generation and the flexibility of load demand,the low carbon economic dispatch problem is modeled as a Markov decision process.The twin delayed deep deterministic policy gradient(TD3)algorithm is used to solve the complex scheduling problem.The effectiveness of the proposed method is verified in the simulation case studies.Compared with TD3,SAC,A3C,DDPG and DQN algorithms,the operating cost is reduced by 8.6%,4.3%,6.1%and 8.0%.

Comparison of Economic Dispatch, OPF and Security Constrained-OPF in Power System Studies RN

Electricity network is a very complex entity that comprises several components like generators, transmission lines, loads among others. As technologies continue to evolve, the complexity of the electricity network has also increased as more devices are being connected to the network. To understand the physical laws governing the operation of the network, techniques such as optimal power flow (OPF), Economic dispatch (ED) and Security constrained optimal power flow (SCOPF) were developed. These techniques have been used extensively in network operation, planning and so on. However, an in-depth presentation showcasing the merits and demerits of these techniques is still lacking in the literature. Hence, this paper intends to fill this gap. In this paper, Economic dispatch, optimal power flow and security-constrained optimal power flow are applied to a 3-bus test system using a linear programming approach. The results of the ED, OPF and SC-OPF are compared and presented.

Solution of Combined Heat and Power Economic Dispatch Problem Using Direct Optimization Algorithm

This paper presents the solution to the combined heat and power economic dispatch problem using a direct solution algorithm for constrained optimization problems. With the potential of Combined Heat and Power (CHP) production to increase the efficiency of power and heat generation simultaneously having been researched and established, the increasing penetration of CHP systems, and determination of economic dispatch of power and heat assumes higher relevance. The Combined Heat and Power Economic Dispatch (CHPED) problem is a demanding optimization problem as both constraints and objective functions can be non-linear and non-convex. This paper presents an explicit formula developed for computing the system-wide incremental costs corresponding with optimal dispatch. The circumvention of the use of iterative search schemes for this crucial step is the innovation inherent in the proposed dispatch procedure. The feasible operating region of the CHP unit three is taken into account in the proposed CHPED problem model, whereas the optimal dispatch of power/heat outputs of CHP unit is determined using the direct Lagrange multiplier solution algorithm. The proposed algorithm is applied to a test system with four units and results are provided.

Dynamic Economic Dispatch for Wind Power System Considering System Security and Spinning Reserve

In this paper, dynamic economic dispatch model is proposed for power systems with bulk wind power integration. The wind turbine generators are assumed to partially undertake the spinning reserve for the thermal generator. A double-layer optimization model is proposed. The outer layer use the differential evolution to search for the power output of thermal generators, and the inner layer use the primal-dual interior point method to solve the OPF of the established output state. Finally, the impact of spinning reserve with wind power on power system operating is validated.

A Multi-Agent Particle Swarm Optimization for Power System Economic Load Dispatch

A new versatile optimization, the particle swarm optimization based on multi-agent system (MAPSO) is presented. The economic load dispatch (ELD) problem of power system can be solved by the algorithm. By competing and cooperating with the randomly selected neighbors, and adjusting its global searching ability and local exploring ability, this algorithm achieves the goal of high convergence precision and speed. To verify the effectiveness of the proposed algorithm, this algorithm is tested by three different ELD cases, including 3, 13 and 40 units IEEE cases, and the experiment results are compared with those tested by other intelligent algorithms in the same cases. The compared results show that feasible solutions can be reached effectively, local optima can be avoided and faster solution can be applied with the proposed algorithm, the algorithm for ELD problem is versatile and efficient.

Risk-averse Robust Interval Economic Dispatch for Power Systems with Large-scale Wind Power Integration

This paper presents a robust interval economic dispatch(RIED)model for power systems with large-scale wind power integration.Differing from existing interval optimization(IO)approaches that merely rely on the upper and lower boundaries of random variables,the distribution information retained in the historical data is introduced to the IO method in this paper.Based on the available probability distribution function(PDF),wind power curtailment and load shedding are quantified as the operational risk and incorporated into the decision-making process.In this model,we need not rely on the forecasted value of wind power,which is randomly fluctuating and quite unpredictable.Furthermore,when the PDFs of wind power are taken into account,the resulting dispatch solution makes a good tradeoff between the generation cost and the operational risk.Finally,the RIED model yields an optimal dispatch solution for thermal units and the allowable intervals of wind power for the wind farms,which efficiently mitigates the uncertainty in wind power generation and provides more practical suggestions for system operators.Simulation studies are conducted on a modified IEEE-118 bus system and the results verify the effectiveness of the proposed RIED model.

Development of electric power cooperation in Northeast Asia

The development of interstate electric ties ISETs and grids(ISGs) is a global process; recently, countries of Northeast Asia(NEA) that were previously very poorly connected have started to study the possibility of constructing ISETs and ISGs. This paper describes the current state of the interstate transmissions in the NEA region, considering the prospective projects of ISETs in NEA. The results of the system optimization study on prospective NEA ISGs are provided. The proposals on the development of electric power cooperation in NEA are formulated.

Design of a Level-3 electric vehicle charging station using a 1-MW solar system via the distributed maximum power point tracking technique

Solar power is mostly influenced by solar irradiation,weather conditions,solar array mismatches and partial shading conditions.Therefore,before installing solar arrays,it is necessary to simulate and determine the possible power generated.Maximum power point tracking is needed in order to make sure that,at any time,the maximum power will be extracted from the photovoltaic system.However,maximum power point tracking is not a suitable solution for mismatches and partial shading conditions.To overcome the drawbacks of maximum power point tracking due to mismatches and shadows,distributed maximum power point tracking is util-ized in this paper.The solar farm can be distributed in different ways,including one DC-DC converter per group of modules or per module.In this paper,distributed maximum power point tracking per module is implemented,which has the highest efficiency.This technology is applied to electric vehicles(EVs)that can be charged with a Level 3 charging station in<1 hour.However,the problem is that charging an EV in<1 hour puts a lot of stress on the power grid,and there is not always enough peak power reserve in the existing power grid to charge EVs at that rate.Therefore,a Level 3(fast DC)EV charging station using a solar farm by implementing distributed maximum power point tracking is utilized to address this issue.Finally,the simulation result is reported using MATLAB®,LTSPICE and the System Advisor Model.Simulation results show that the proposed 1-MW solar system will provide 5 MWh of power each day,which is enough to fully charge~120 EVs each day.Additionally,the use of the proposed photovoltaic system benefits the environment by removing a huge amount of greenhouse gases and hazardous pollutants.For example,instead of supplying EVs with power from coal-fired power plants,1989 pounds of CO_(2) will be eliminated from the air per hour.

The Opportunities and Cooperation for Electric Power Enterprises on the New Silk Road Economic Belt

New Silk Road Economic Belt has an important strategic significance for European and Asian economic integration, which is a concept that formed on the ancient Silk Road and is about of contemporary economic and trade cooperation. The focus of New Silk Road Economic Belt integration strategy implementation is the energy integration. The key areas in energy development are electricity, oil and gas. This article analyzes the opportunities and challenges for the power equipment manufacturing enterprises, power generation enterprises and electric grid enterprises on the New Silk Road Economic Belt. This article also emphasizes the importance of seizing the development opportunities and strengthening the international power cooperation.

Stochastic Economic Dispatch Based Optimal Market Clearing Strategy Considering Flexible Ramping Products Under Wind Power Uncertainties

High penetration level of renewable energy has brought great challenges to operation of power systems,and use of flexible resources(FRs)is becoming increasingly important.Flexibility of power systems can be improved by changing generation arrangements,but the interests of some market participants may be harmed in the process.This study proposes a stochastic economic dispatch model with trading of flexible ramping products(FRPs).To calculate changes in revenue and reasonably compensate units that provide FRs,multisegmented marginal bidding for energy is simulated by linearizing generation cost,and an optimal market clearing strategy for FRPs is developed according to changes in clearing energy and marginal clearing price.Then,the correlation between prediction errors of wind speeds among different wind farms is determined based on a joint distribution function modeled by the copula function,and quasi-Monte Carlo simulation(QMC)is used to generate wind power scenarios.Finally,numerical simulations of modified IEEE-30 and IEEE-118 bus systems is performed with minimum comprehensive cost as the objective function.This verifies the proposed model could effectively deal with wind variability and uncertainty,stabilize the marginal clearing price of the electricity market,and ensure fairness in the market.

A Chance Constrained Optimal Reserve Scheduling Approach for Economic Dispatch Considering Wind Penetration

The volatile wind power generation brings a full spectrum of problems to power system operation and management, ranging from transient system frequency fluctuation to steady state supply and demand balancing issue. In this paper, a novel wind integrated power system day-ahead economic dispatch model, with the consideration of generation and reserve cost is modelled and investigated. The proposed problem is first formulated as a chance constrained stochastic nonlinear programming U+0028 CCSNLP U+0029, and then transformed into a deterministic nonlinear programming U+0028 NLP U+0029. To tackle this NLP problem, a three-stage framework consists of particle swarm optimization U+0028 PSO U+0029, sequential quadratic programming U+0028 SQP U+0029 and Monte Carlo simulation U+0028 MCS U+0029 is proposed. The PSO is employed to heuristically search the line power flow limits, which are used by the SQP as constraints to solve the NLP problem. Then the solution from SQP is verified on benchmark system by using MCS. Finally, the verified results are feedback to the PSO as fitness value to update the particles. Simulation study on IEEE 30-bus system with wind power penetration is carried out, and the results demonstrate that the proposed dispatch model could be effectively solved by the proposed three-stage approach. © 2017 Chinese Association of Automation.

A Comprehensive Analysis of Plug in Hybrid Electric Vehicles to Commercial Campus (V2C)

Vehicle to grid is an emerging technology that utilizes plug in hybrid electric vehicle batteries to benefit electric utilities during times when the vehicle is parked and connected to the electric grid. In its current form however, vehicle to grid implementation poses many challenges that may not be easily overcome and many existing studies neglect critical aspects such as battery cost or driving profiles. The goal of this research is to ease some of these challenges by examining a vehicle to grid scenario on a university campus, as an example of a commercial campus, based on time of use electricity rates. An analysis of this scenario is conducted on a vehicle battery as well as a stationary battery for comparison. It is found that vehicle to campus and a stationary battery both have the potential to prove economical based on battery cost and electricity rates.

Two-stage stochastic approach for spinning reserve allocation in dynamic economic dispatch

A novel approach was proposed to allocate spinning reserve for dynamic economic dispatch.The proposed approach set up a two-stage stochastic programming model to allocate reserve.The model was solved using a decomposed algorithm based on Benders' decomposition.The model and the algorithm were applied to a simple 3-node system and an actual 445-node system for verification,respectively.Test results show that the model can save 84.5 US $ cost for the testing three-node system,and the algorithm can solve the model for 445-node system within 5 min.The test results also illustrate that the proposed approach is efficient and suitable for large system calculation.