Power Dispatch Strategy Research Articles

Many-Objective Optimal Power Dispatch Strategy Incorporating Temporal and Spatial Distribution Control of Multiple Air Pollutants

Traditional economic/environment dispatch usually aims to reduce emission, but neglects air pollutant dispersion and lacks target-oriented control. This paper proposes a many-objective optimal power dispatch (MaOOPD) strategy incorporating temporal and spatial distribution (TSD) control of multiple air pollutants. Ground level concentration, i.e., the TSD, of air pollutant is estimated by a novel air pollutant dispersion model specifically constructed for thermal power plants. A MaOOPD model is developed to cut generation cost, reduce CO2 emission, and control the TSD of PM2.5, SO2, and NO2 simultaneously. To determine a compromise solution, a decision-making method which exploits atmospheric environmental capacity margin is employed, so that actual needs of atmospheric environmental protection are captured. Case studies indicate that not only can the strategy reduce air pollution effectively, but also it can achieve a dynamic tradeoff among the objectives as weather conditions and background pollution vary, leading to an authentic economic environmental power dispatch.

Optimal reactive power dispatch of permanent magnet synchronous generator-based wind farm considering levelised production cost minimisation

As wind power penetration increases, large wind farms (WFs) need to provide reactive power according to modern grid codes. Permanent magnet synchronous generator-based wind turbines (WTs) can generate reactive power, by assigning the appropriate reactive power to each WT to meet the reactive power requirements of the grid. This is a more economical method than setting up additional reactive power compensation equipment. This study proposes an optimal reactive power dispatch strategy for minimising a levelised production cost, and is implemented in two ways: minimising the power loss of a WF, and maximising the lifetime of WTs. The reactive power references of each WT are chosen as the optimisation variables, and a particle swarm optimisation algorithm is adopted to solve the optimisation problem. The proposed and traditional reactive power dispatch strategies are demonstrated and compared on a WF with 25 WTs to validate the effectiveness of the proposed approach.

Optimal power dispatch considering load and renewable generation uncertainties in an AC–DC hybrid microgrid

An AC-DC hybrid microgrid is gradually becoming popular. For economic viability and environmental sustainability, an AC-DC microgrid should be operated optimally. This study introduces an optimal power dispatch strategy for simultaneous reduction of cost and emission from generation activities in an AC-DC hybrid microgrid under load and generation uncertainties. The operational attributes of an AC-DC hybrid microgrid, and load and renewable generation uncertainties are incorporated in the optimal scheduling problem by using a customised power-flow technique, and by modelling uncertainties by Hong's two-point estimate method, respectively. The economic and environmental objectives are modelled in the fuzzy domain by fuzzy membership functions. A combination of particle swarm optimisation and fuzzy max-min technique is then employed for obtaining the optimal solution. The static active power droop constants of the dispatchable units are the control variables. Simulation results on a 6-bus AC-DC hybrid microgrid system demonstrate that optimal scheduling results in 4.26% reduction of operating cost and 13.91% reduction of emission in comparison to capacity based droop settings. Further, a comparison between the proposed method and the elitist multi-objective GA indicates that the optimised solution lies on the Pareto-front, thereby validating the proposed technique.

Optimal Power Dispatch of an Offshore Wind Farm under Generator Fault

For offshore wind farms, the power loss caused by the wake effect is large due to the large capacity of the wind turbine. At the same time, the operating environment of the offshore wind farm is very harsh, and the cost of maintenance is higher than that of the onshore wind farm. Therefore, it is worthwhile to study through reasonable control how to reduce the wake loss of the wind farm and minimize the losses caused by the fault. In this paper, the Particle Swarm Optimization (PSO) algorithm is used to optimize the active power dispatch of wind farms under generator cooling system faults. The optimization objectives include avoiding the further deterioration of the generator fault, reducing unnecessary power loss of the faulty wind turbine, tracking the power demand from the Transmission System Operator (TSO), and reducing the power fluctuation caused by the PSO algorithm. The proposed optimal power dispatch strategy was compared with the two generally-used fault-handling methods and the proportional dispatch strategy in simulation. The result shows that the proposed strategy can improve the power generation capacity of the wind farm and achieve an efficient trade-off between power generation and fault protection.

Daily Operation Optimization of a Hybrid Energy System Considering a Short-Term Electricity Price Forecast Scheme

The scenario where the renewable generation penetration is steadily on the rise in an increasingly atomized system, with much of the installed capacity “sitting” on a distribution level, is in clear contrast with the “old paradigm” of a natural oligopoly formed by vertical structures. Thereby, the fading of the classical producer–consumer division to a broader prosumer “concept” is fostered. This crucial transition will tackle environmental harms associated with conventional energy sources, especially in this age where a greater concern regarding sustainability and environmental protection exists. The “smoothness” of this transition from a reliable conventional generation mix to a more volatile and “parti-colored" one will be particularly challenging, given escalating electricity demands arising from transportation electrification and proliferation of demand-response mechanisms. In this foreseeable framework, proper Hybrid Energy Systems sizing, and operation strategies will be crucial to dictate the electric power system’s contribution to the “green” agenda. This paper presents an optimal power dispatch strategy for grid-connected/off-grid hybrid energy systems with storage capabilities. The Short-Term Price Forecast information as an important decision-making tool for market players will guide the cost side dispatch strategy, alongside with the storage availability. Different scenarios were examined to highlight the effectiveness of the proposed approach.

Model Predictive Power Dispatch and Control With Price-Elastic Load in Energy Internet

The safety and stability of modern power systems are undergoing various challenges, introduced by the integration of fluctuating renewable generation. In this paper, we present a hierarchical model predictive power dispatch and control strategy for a class of modern power systems with price-elastic controllable loads (CLs) in energy Internet. In the upper-level optimization, a generalized multiperiod economic dispatch (GMPED) problem is organized within an electricity market environment aiming at maximizing the social welfare. Specifically, the price-elastic CLs are aggregated in controllable load aggregators (CLAs) to participate in the market competition. A novel utility function of the price-elastic CLAs is proposed for market demand response. By solving GMPED, the power setpoints of plants over the further periods are produced, as well as the real-time price for the optimal response of price-elastic CLAs. In the second-level operation, two types of model predictive control-based controllers for both the supply and demand sides are designed for power tracking control by considering the model of the power system and aggregated thermostatically controlled loads. Finally, two case studies are performed on the IEEE 14- and 39-bus system, respectively, which shows that the system-frequency deviation and system cost are reduced significantly with the proposed methods.

Two-Stage Dynamic Reactive Power Dispatch Strategy in Distribution Network Considering the Reactive Power Regulation of Distributed Generations

To solve the coordinated dispatch and time-space coupling problem in the distribution network with distributed generations (DGs), a novel two-stage (Heuristic search and Variable correction) dynamic reactive power dispatch strategy is proposed in this paper. Meanwhile, according to the analysis of Gauss-type and Z-type membership function, a new objective function is put forward to achieve the balance between network loss and voltage deviation. First, the pre-optimization is made in Niche genetic algorithm to coordinate on-load tap changer (OLTC), capacitor banks (CBs), and DGs. Second, the sequential fuzzy c-means (SFCM) based on artificial bee colony (ABC) algorithm is proposed and then the Heuristic search based on this method is used to formulate the day-ahead plans of OLTC and CBs. Finally, the dispatch plans of OLTC and CBs are applied to recalibrate the day-ahead dispatch plans of DGs. Comparing with existing schemes, strategies and optimization approaches based on diverse evaluation indicators, optimization results on IEEE 33-bus, IEEE 14-bus, and PG&E 69-bus test systems verify the practicality and efficiency of the proposed method. In addition, the effects on the optimization results with different adjustment step and action time of the discrete adjust devices are discussed by the IEEE 33-bus test system.

Optimal reactive power dispatch of a full-scale converter based wind farm considering loss minimization

With the development of wind power, wind farms are required to provide reactive power to the power system. For permanent magnet synchronous generator (PMSG) based large wind farms (WF), it may be an economical way to generate reactive power using the power electronic devices inside each wind turbine (WT). In this paper, an optimal reactive power dispatch of PMSG WF is proposed to minimize the power loss. Both the losses inside WTs and the losses of transmission system are all considered. Particle swarm optimization (PSO) algorithm is adopted to find the reactive power references of each WT which makes the total loss of WF minimal. A WF with 25 5 MW PMSG WTs arranged in 5 rows and 5 columns is used in the case study. And two traditional reactive power dispatch strategies are compared comprehensively with the proposed strategy at different scenarios, the results have shown that the proposed strategy obtains lower power loss than the other two traditional strategies in all the studied cases.

Optimal reactive power dispatch of renewable energy based on primal-dual interior point filter method

This paper proposes an optimal reactive power dispatch strategy of the renewable energy. It coordinates the different reactive power sources by considering the topological structure and the feeder lines impedance of the renewable energy. The primal-dual interior point filter method is utilized to solve such a nonlinear optimization problem and bind the voltage of the point of common coupling of the renewable energy to the reference value. The proposed strategy gives priority to the reactive power output of wind turbine generators to ensure the dynamic compensation devices could have enough reactive power reserves and meanwhile equipoises terminal voltage profiles among all the controlled wind turbine generators. The simulation results of an actual renewable energy in North China validate the control performance of the proposed optimization model and the effectiveness of the primal-dual interior point filter method.

Agent-Based Recovery Model for Seismic Resilience Evaluation of Electrified Communities.

In this article, an agent-based framework to quantify the seismic resilience of an electric power supply system (EPSS) and the community it serves is presented. Within the framework, the loss and restoration of the EPSS power generation and delivery capacity and of the power demand from the served community are used to assess the electric power deficit during the damage absorption and recovery processes. Damage to the components of the EPSS and of the community-built environment is evaluated using the seismic fragility functions. The restoration of the community electric power demand is evaluated using the seismic recovery functions. However, the postearthquake EPSS recovery process is modeled using an agent-based model with two agents, the EPSS Operator and the Community Administrator. The resilience of the EPSS-community system is quantified using direct, EPSS-related, societal, and community-related indicators. Parametric studies are carried out to quantify the influence of different seismic hazard scenarios, agent characteristics, and power dispatch strategies on the EPSS-community seismic resilience. The use of the agent-based modeling framework enabled a rational formulation of the postearthquake recovery phase and highlighted the interaction between the EPSS and the community in the recovery process not quantified in resilience models developed to date. Furthermore, it shows that the resilience of different community sectors can be enhanced by different power dispatch strategies. The proposed agent-based EPSS-community system resilience quantification framework can be used to develop better community and infrastructure system risk governance policies.

Multi area power dispatch strategy considering economic and environmental aspects using NDSGA II

Multi area economic environmental dispatch strategies (MAEEDS), corresponding to centralised fossil fuel fired power plants, remain significant for allocating power among committed units of various regions in an optimalised manner. The allocation must ensure simultaneous reduction in total fuel cost and emission level in the best possible ways. At the same time, equality alongside inequality constraints likes production-demand balance, power production capacity and tie line capacity have to be taken into consideration. The aforementioned optimalisation task involves multiple objective optimisation with contradictory behaving goals. The presented article suggests non-dominated sorting genetic algorithm II (NDSGA II) to achieve solutions for the MAEEDS task. A comparison has also been made among the solutions attained through the suggested technique, strength Pareto evolutionary algorithm II (STPEA II) and other well-known optimisation techniques available in the literature. The comparative analysis shows superiority of the suggested NDSGA II for the considered four area MAEEDS task.

Multi area power dispatch strategy considering economic and environmental aspects using NDSGA II

Multi area economic environmental dispatch strategies (MAEEDS), corresponding to centralised fossil fuel fired power plants, remain significant for allocating power among committed units of various regions in an optimalised manner. The allocation must ensure simultaneous reduction in total fuel cost and emission level in the best possible ways. At the same time, equality alongside inequality constraints likes production-demand balance, power production capacity and tie line capacity have to be taken into consideration. The aforementioned optimalisation task involves multiple objective optimisation with contradictory behaving goals. The presented article suggests non-dominated sorting genetic algorithm II (NDSGA II) to achieve solutions for the MAEEDS task. A comparison has also been made among the solutions attained through the suggested technique, strength Pareto evolutionary algorithm II (STPEA II) and other well-known optimisation techniques available in the literature. The comparative analysis shows superiority of the suggested NDSGA II for the considered four area MAEEDS task.

Optimized Power Dispatch in Wind Farms for Power Maximizing Considering Fatigue Loads

Wake effects in a wind farm (WF) include the wind velocity deficit and the added turbulence. The wind velocity deficit may bring significant loss of the wind power and the added turbulence may cause extra fatigue load on the wind turbines (WTs). Inclusion of the wake effects in the wind farm control design can increase the total captured power by derating the upwind WTs. However, this may increase the turbulence and cause more fatigue load on the downwind WTs. This paper proposes an optimized active power dispatch strategy for WFs to maximize the total captured power while maintaining the fatigue load of the shafts and the towers within a certain range from the values using traditional strategy, which adopts maximum power point tracking (MPPT) control for each WT. A WT derating control strategy is included in the WT controller and the fatigue load for the tower and shaft is evaluated offline at a series of turbulence intensity, mean wind speed and active power reference to form a lookup table, which is used for the WF control. The proposed strategy is compared with WT MPPT control strategy and WF MPPT control strategy. The simulation results show the effectiveness of the proposed strategy.

A market‐oriented wind power dispatch strategy using adaptive price thresholds and battery energy storage

AbstractIn this paper, an adaptive dispatch strategy is presented to maximize the revenue for grid‐tied wind power plant coupled with a battery energy storage system (BESS). The proposed idea is mainly based on time‐varying market‐price thresholds, which are varied according to the proposed algorithm in an adaptive manner. The variable nature of wind power and market price signals leads to the idea of storing energy at low price periods and consequently selling it at high prices. In fact, the wind farm operators can take advantage of the price variability to earn additional income and to maximize the operational profit based on the choice of best price thresholds at each instant of time. This research study proposes an efficient strategy for intermittent power dispatch along with the optimal operation of a BESS in the presence of physical limits and constraints. The strategy is tested and validated with different BESSs, and the percentage improvement of income is calculated. The simulation results, based on actual wind farm and market‐price data, depict the proficiency of the proposed methodology over standard linear programming methods.

Power Dispatch Strategy for Interconnected Microgrids Based Hybrid Renewable Energy System

This paper proposes a studied system made up of interconnected microgrids (MGs) in which each one includes a hybrid renewable energy system (HRES). The proposed power dispatch strategy ensures load satisfaction in each MG by maintaining the equilibrium between production and consumer demand powers via interactions among MGs and the utility grid. The purpose of this paper is to provide an adequate supply to the different MGs by adapting production to consumption according to the available hybrid power production. This is done with the consideration of the dispatch of the power exchanged among MGs and the main grid. The cooperation between the multiples MGs ensures higher resilience and flexibility of the whole distribution system. The case study results are provided to show the feasibility of this proposed power dispatch strategy in the smart grid environment.

On maximizing profit of wind-battery supported power station based on wind power and energy price forecasting

This paper proposes a framework to develop an optimal power dispatch strategy for grid-connected wind power plants containing a Battery Energy Storage System (BESS). Considering the intermittent nature of wind power and rapidly varying electricity market price, short-term forecasting of these variables is used for efficient energy management. The predicted variability trends in market price assist in earning additional income which subsequently increase the operational profit. Then on the basis of income improvement, optimal capacity of the BESS can be determined. The proposed framework utilizes Dynamic Programming tool which can incorporate the predictions of both wind power and market price simultaneously as inputs in a receding horizon approach. The proposed strategy is validated using real electricity market price and wind power data in different scenarios of BESS power and capacity. The obtained results depict the effectiveness of the strategy to help power system operators in ensuring economically optimal energy dispatch. Moreover, the results can aid power system planners in the selection of optimal BESS capacity for given power ratings in order to maximize their operational profits.

Review of Reactive Power Dispatch Strategies for Loss Minimization in a DFIG-based Wind Farm

This paper reviews and compares the performance of reactive power dispatch strategies for the loss minimization of Doubly Fed Induction Generator (DFIG)-based Wind Farms (WFs). Twelve possible combinations of three WF level reactive power dispatch strategies and four Wind Turbine (WT) level reactive power control strategies are investigated. All of the combined strategies are formulated based on the comprehensive loss models of WFs, including the loss models of DFIGs, converters, filters, transformers, and cables of the collection system. Optimization problems are solved by a Modified Particle Swarm Optimization (MPSO) algorithm. The effectiveness of these strategies is evaluated by simulations on a carefully designed WF under a series of cases with different wind speeds and reactive power requirements of the WF. The wind speed at each WT inside the WF is calculated using the Jensen wake model. The results show that the best reactive power dispatch strategy for loss minimization comes when the WF level strategy and WT level control are coordinated and the losses from each device in the WF are considered in the objective.

Optimal design and dispatch of a system of diesel generators, photovoltaics and batteries for remote locations

Renewable energy technologies, specifically, solar photovoltaic cells, combined with battery storage and diesel generators, form a hybrid system capable of independently powering remote locations, i.e., those isolated from larger grids. If sized correctly, hybrid systems reduce fuel consumption compared to diesel generator-only alternatives. We present an optimization model for establishing a hybrid power design and dispatch strategy for remote locations, such as a military forward operating base, that models the acquisition of different power technologies as integer variables and their operation using nonlinear expressions. Our cost-minimizing, nonconvex, mixed-integer, nonlinear program contains a detailed battery model. Due to its complexities, we present linearizations, which include exact and convex under-estimation techniques, and a heuristic, which determines an initial feasible solution to serve as a “warm start” for the solver. We determine, in a few hours at most, solutions within 5% of optimality for a candidate set of technologies; these solutions closely resemble those from the nonlinear model. Our instances contain real data spanning a yearly horizon at hour fidelity and demonstrate that a hybrid system could reduce fuel consumption by as much as 50% compared to a generator-only solution.

Offshore Wind Farm Layout Design Considering Optimized Power Dispatch Strategy

Offshore wind farm has drawn more and more attention recently due to its higher energy capacity and more freedom to occupy area. However, the investment is higher. In order to make a cost-effective wind farm, the wind farm layout should be optimized. The wake effect is one of the dominant factors leading to energy losses. It is expected that the optimized placement of wind turbines (WT) over a large sea area can lead to the best tradeoff between energy yields and capital investment. This paper proposes a novel way to position offshore WTs for a regular shaped wind farm. In addition to optimizing the direction of wind farm placement and the spacing between WTs, the control strategy's impact on energy yields is also discussed. Since the problem is nonconvex and lots of optimization variables are involved, an evolutionary algorithm, the particle swarm optimization algorithm, is adopted to find the solution. In order to increase the probability of finding the global optimal solution, the adaptive parameter control strategy is utilized. Simulation results are given to verify the proposed approach and comparison is made with results obtained using other methods.

Optimization-based reactive power control in HVDC-connected wind power plants

One application of high–voltage dc (HVdc) systems is the connection of remotely located offshore wind power plants (WPPs). In these systems, the offshore WPP grid and the synchronous main grid operate in decoupled mode, and the onshore HVdc converter fulfills the grid code requirements of the main grid. Thus, the offshore grid can be operated independently during normal conditions by the offshore HVdc converter and the connected wind turbines. In general, it is well known that optimized reactive power allocation might lower the component loading and power losses. This paper aims to propose and assess a reactive power allocation optimization within HVdc–connected WPPs. For these systems, the offshore converter operates the adjoining grid by imposing frequency and voltage. The reference voltage magnitude is used as additional control variable for the optimization algorithm. The loss function incorporates both the collection grid and the converter losses. The use of the proposed strategy results in an effective reduction of losses compared to conventional reactive power dispatch strategies alongside with improvements of the voltage profile. A case study for a 500 MW–sized WPP demonstrates an additional annual energy production of 6819 MWh or an economical benefit of 886 k€yr−1 when using the proposed strategy.