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.

Wind farm active power dispatching algorithm based on Grey Incidence

This study proposes a wind farm active power dispatching(WFAPD) algorithm based on the grey incidence method, which does not rely on an accurate mathematical model of wind turbines. Based on the wind turbine start-stop data at different wind speeds, the weighting coefficients, which are the participation degrees of a variable speed system and a variable pitch system in power regulation, are obtained using the grey incidence method. The incidence coefficient curve is fitted by the B-spline function at a full range of wind speeds, and the power regulation capacity of all wind turbines is obtained. Finally, the WFAPD algorithm, which is based on the regulating capacity of each wind turbine, is compared with the wind speed weighting power dispatching(WSWPD) algorithm in MATLAB. The simulation results show that the active power fluctuation of the wind farm is smaller, the rotating speed of wind turbines is smoother, and the fatigue load of highspeed turbines is effectively reduced.

Framework and Key Technologies of Human-machine Hybrid-augmented Intelligence System for Large-scale Power Grid Dispatching and Control

With integration of large-scale renewable energy,new controllable devices,and required reinforcement of power grids,modern power systems have typical characteristics such as uncertainty,vulnerability and openness,which makes operation and control of power grids face severe security challenges.Application of artificial intelligence(AI)technologies represented by machine learning in power grid regulation is limited by reliability,interpretability and generalization ability of complex modeling.Mode of hybrid-augmented intelligence(HAI)based on human-machine collaboration(HMC)is a pivotal direction for future development of AI technology in this field.Based on characteristics of applications in power grid regulation,this paper discusses system architecture and key technologies of human-machine hybrid-augmented intelligence(HHI)system for large-scale power grid dispatching and control(PGDC).First,theory and application scenarios of HHI are introduced and analyzed;then physical and functional architectures of HHI system and human-machine collaborative regulation process are proposed.Key technologies are discussed to achieve a thorough integration of human/machine intelligence.Finally,state-of-theart and future development of HHI in power grid regulation are summarized,aiming to efficiently improve the intelligent level of power grid regulation in a human-machine interactive and collaborative way.

Parallel Dispatch:A New Paradigm of Electrical Power System Dispatch

Modern power systems are evolving into sociotechnical systems with massive complexity, whose real-time operation and dispatch go beyond human capability. Thus,the need for developing and applying new intelligent power system dispatch tools are of great practical significance. In this paper, we introduce the overall business model of power system dispatch, the top level design approach of an intelligent dispatch system, and the parallel intelligent technology with its dispatch applications. We expect that a new dispatch paradigm,namely the parallel dispatch, can be established by incorporating various intelligent technologies, especially the parallel intelligent technology, to enable secure operation of complex power grids,extend system operators' capabilities, suggest optimal dispatch strategies, and to provide decision-making recommendations according to power system operational goals.

Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs

Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services.In recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power grids.With multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision supports.To achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical step.Existing entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best performance.To address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart assignments.This model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational similarities.Then,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one alignments.We not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual KGs.Experimental comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.

Evidence Mechanism of Power Dispatching Instruction Based on Blockchain

With the development and application of energy Internet technology,the collaborative interaction of“source network,load and storage”has becomethe development trend of power grid dispatching.The large-scale access of renewableenergy on the load side,the unified management of adjustable loads,and theparticipation of multiple parties in energy operations have put forward requirementsfor the safety,credibility,openness,and transparency of the load dispatchingenvironment.Under the environment of carbon emission reduction,the paperproposed an architecture of the scheduling data blockchain,based on the in-depthstudy of blockchain.Moreover,smart contracts are used to realize the applicationscenario of load dispatching instruction evidence on the blockchain.The contentand storage mode of scheduling instruction evidence on blockchain are studied.And different storage modes are adopted according to the actual needs.Andthe smart contract system realizes the evidence generation of power dispatchinginstruction.This is the basis for the normal circulation of power dispatchinginstruction evidence.The research significance of this paper is highlighted as follows.The data and information generated in the power dispatching process arestored as evidence.On the one hand,it can provide a basis for settlement betweenpower production and dispatching companies and power users.On the other hand,it can prepare for distributed transactions in the power grid under the environmentof carbon emission reduction.

Short-term power generation scheduling rules for cascade hydropower stations based on hybrid algorithm

Power generation dispatching is a large complex system problem with multi-dimensional and nonlinear characteristics. A mathematical model was established based on the principle of reservoir operation. A large quantity of optimal scheduling processes were obtained by calculating the daily runoff process within three typical years, and a large number of simulated daily runoff processes were obtained using the progressive optimality algorithm （POA） in combination with the genetic algorithm （GA）. After analyzing the optimal scheduling processes, the corresponding scheduling rules were determined, and the practical formulas were obtained. These rules can make full use of the rolling runoff forecast and carry out the rolling scheduling. Compared with the optimized results, the maximum relative difference of the annual power generation obtained by the scheduling rules is no more than 1%. The effectiveness and practical applicability of the scheduling rules are demonstrated by a case study. This study provides a new perspective for formulating the rules of power generation dispatching.

Hierarchical Cluster Coordination Control Strategy for Large-scale Wind Power Based on Model Predictive Control and Improved Multi-time-scale Active Power Dispatching

The grid-connection of large-scale and high-penetration wind power poses challenges to the friendly dispatching control of the power system.To coordinate the complicated optimal dispatching and rapid real-time control,this paper proposes a hierarchical cluster coordination control(HCCC)strategy based on model predictive control(MPC)technique.Considering the time-varying characteristics of wind power generation,the proposed HCCC strategy constructs an improved multitime-scale active power dispatching model,which consists of five parts:formulation of cluster dispatching plan,rolling modification of intra-cluster plan,optimization allocation of wind farm(WF),grouping coordinated control of wind turbine group(WTG),and real-time adjustment of single-machine power.The time resolutions are sequentially given as 1 hour,30 min,15 min,5 min,and 1 min.In addition,a combined predictive model based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),wavelet thresholding(WT),and least squares support vector machine(LSSVM)is established.The fast predictive feature of this model cooperates with the HCCC strategy that effectively improves the predictive control precision.Simulation results show that the proposed HCCC strategy enables rapid response to active power control(APC),and significantly improves dispatching control accuracy and wind power accommodation capabilities.

Optimal reactive power dispatch of wind power plant cluster considering static voltage stability for low-carbon power system

The implementation of developing the wind power is an important way to achieve the low-carbon power system.However,the voltage stability issues caused by the random fluctuations of active power output and the irrational regulations of reactive power compensation equipment have become the prominent problems of the regions where large-scale wind power integrated.In view of these problems,this paper proposed an optimal reactive power dispatch(ORPD)strategy of wind power plants cluster(WPPC)considering static voltage stability for lowcarbon power system.The control model of the ORPD strategy was built according to the wind power prediction,the present operation information and the historical operation information.By utilizing the automatic voltage control capability of wind power plants and central substations,the ORPD strategy can achieve differentiated management between the discrete devices and the dynamic devices of the WPPC.Simulation results of an actual WPPC in North China show that the ORPD strategy can improve the voltage control performance of the pilot nodes and coordinate the operation between discrete devices and the dynamic devices,thus maintaining the static voltage stability as well.

Strategy of constructing virtual peaking unit by public buildings’central air conditioning loads for day-ahead power dispatching

With the gradually widely usage of the air conditioning(AC) loads in developing countries, the urban power grid load has swiftly increased over the past decade.Especially in China, the AC load has accounted for over30% of the maximum load in many cities during summer.This paper proposes a scheme of constructing a virtual peaking unit(VPU) by public buildings’ cool storage central AC(CSCAC) systems and non-CSCAC(NCSCAC)systems for the day-ahead power network dispatching(DAPND). Considering the accumulation effect of different meteorological parameters, a short term load forecasting method of public building’s central AC(CAC) baseline load is firstly discussed. Then, a second-order equivalent thermal parameters model is established for the public building’s CAC load. Moreover, the novel load reduction control strategies for the public building’s CSCAC system and the public building’s NCSCAC system are respectively presented. Furthermore, based on the multiple-rank control strategy, the model of the DAPND with the participation of a VPU is set up. The VPU is composed of large-scale regulated public building’s CAC loads. To demonstrate the effectiveness of the proposed strategy, results of a sample study on a region in Nanjing which involves 22 public buildings’ CAC loads are described in this paper. Simulated results show that, by adopting the proposed DAPND scheme, the power network peak load in the region obviously decreases with a small enough deviation between the regulated load value and the dispatching instruction of the VPU. The total electricity-saving amount accounts for7.78% of total electricity consumption of the VPU before regulation.

Distributed Resource Allocation via Accelerated Saddle Point Dynamics

In this paper,accelerated saddle point dynamics is proposed for distributed resource allocation over a multi-agent network,which enables a hyper-exponential convergence rate.Specifically,an inertial fast-slow dynamical system with vanishing damping is introduced,based on which the distributed saddle point algorithm is designed.The dual variables are updated in two time scales,i.e.,the fast manifold and the slow manifold.In the fast manifold,the consensus of the Lagrangian multipliers and the tracking of the constraints are pursued by the consensus protocol.In the slow manifold,the updating of the Lagrangian multipliers is accelerated by inertial terms.Hyper-exponential stability is defined to characterize a faster convergence of our proposed algorithm in comparison with conventional primal-dual algorithms for distributed resource allocation.The simulation of the application in the energy dispatch problem verifies the result,which demonstrates the fast convergence of the proposed saddle point dynamics.

Aggregated impact of allowance allocation and power dispatching on emission reduction

Climate change has become one of the most important issues for the sustainable development of social well-being.China has made great efforts in reducing CO2 emissions and promoting clean energy.Pilot Emission Trading Systems(ETSs)have been launched in two provinces and five cities in China,and a national level ETS will be implemented in the third quarter of 2017,with preparations for China’s national ETS now well under way.In the meantime,a new round of China’s electric power system reform has entered the implementation stage.Policy variables from both electricity and emission markets willimpose potential risks on the operation of generation companies(Gen Cos).Under this situation,by selecting key variables in each domain,this paper analyzes the combined effects of different allowance allocation methods and power dispatching models on power system emission.Key parameters are set based on a provincial power system in China,and the case studies are conducted based on dynamic simulation platform for macro-energy systems(DSMES)software developed by the authors.The selected power dispatching models include planned dispatch,energy saving power generation dispatch and economic dispatch.The selected initial allowance allocation methods in the emission market include the grandfathering method based on historical emissions and the benchmarking method based on actual output.Based on the simulation results and discussions,several policy implications are highlighted to help to design an effective emission market in China.

Short-term Scheduling of Steam Power System in Iron and Steel Industry under Time-of-use Power Price

A generalized formulation for short-term scheduling of steam power system in iron and steel industry under the time-of-use（TOU）power price was presented,with minimization of total operational cost including fuel cost,equipment maintenance cost and the charge of exchange power with main grid.The model took into account the varying nature of surplus byproduct gas flows,several practical technical constraints and the impact of TOU power price.All major types of utility equipments,involving boilers,steam turbines,combined heat and power（CHP）units,and waste heat and energy recovery generators（WHERG）,were separately modeled using thermodynamic balance equations and regression method.In order to solve this complex nonlinear optimization model,a new improved particle swarm optimization（IPSO）algorithm was proposed by incorporating time-variant parameters,a selfadaptive mutation scheme and efficient constraint handling strategies.Finally,a case study for a real industrial example was used for illustrating the model and validating the effectiveness of the proposed approach.

Optimal Active Power Dispatching of Microgrid and Distribution Network Based on Model Predictive Control

First, a three-tier coordinated scheduling system consisting of a distribution network dispatch layer, a microgrid centralized control layer, and local control layer in the energy internet is proposed. The multi-time scale optimal scheduling of the microgrid based on Model Predictive Control（MPC） is then studied, and the optimized genetic algorithm and the microgrid multi-time rolling optimization strategy are used to optimize the datahead scheduling phase and the intra-day optimization phase. Next, based on the three-tier coordinated scheduling architecture, the operation loss model of the distribution network is solved using the improved branch current forward-generation method and the genetic algorithm. The optimal scheduling of the distribution network layer is then completed. Finally, the simulation examples are used to compare and verify the validity of the method.

Multi-timescale Affinely Adjustable Robust Reactive Power Dispatch of Distribution Networks Integrated with High Penetration of PV

Photovoltaic(PV)power generation has highly penetrated in distribution networks,providing clean and sustainable energy.However,its uncertain and intermittent power outputs significantly impair network operation,leading to unexpected power loss and voltage fluctuation.To address the uncertainties,this paper proposes a multi-timescale affinely adjustable robust reactive power dispatch(MTAAR-RPD)method to reduce the network power losses as well as alleviate voltage deviations and fluctuations.The MTAAR-RPD aims to coordinate on-load tap changers(OLTCs),capacitor banks(CBs),and PV inverters through a three-stage structure which covers multiple timescales of“hour-minute-second”.The first stage schedules CBs and OLTCs hourly while the second stage dispatches the base reactive power outputs of PV inverter every 15 min.The third stage affinely adjusts the inverter reactive power output based on an optimized Q-P droop controller in real time.The three stages are coordinately optimized by an affinely adjustable robust optimization method.A solution algorithm based on a cutting plane algorithm is developed to solve the optimization problem effectively.The proposed method is verified through theoretical analysis and numerical simulations.

Stochastic Accelerated Alternating Direction Method of Multipliers for Hedging Communication Noise in Combined Heat and Power Dispatch

Combined heat and power dispatch(CHPD)opens a new window for increasing operational flexibility and reducing wind power curtailment.Electric power and district heating systems are independently controlled by different system operators;therefore,a decentralized solution paradigm is necessary for CHPD,in which only minor boundary information is required to be exchanged via a communication network.However,a nonideal communication environment with noise could lead to divergence or incorrect solutions of decentralized algorithms.To bridge this gap,this paper proposes a stochastic accelerated alternating direction method of multipliers(SA-ADMM)for hedging communication noise in CHPD.This algorithm provides a general framework to address more types of constraint sets and separable objective functions than the existing stochastic ADMM.Different from the single noise sources considered in the existing stochastic approximation methods,communication noise from multiple sources is addressed in both the local calculation and the variable update stages.Case studies of two test systems validate the effectiveness and robustness of the proposed SAADMM.

Time-efficient Strategic Power Dispatch for District Cooling Systems Considering Evolution of Cooling Load Uncertainties

District cooling system(DCS)provides centralized chilled water to multiple buildings for air conditioning with high energy-efficiency and operational flexibility.It is one of the most popular cooling systems for large buildings in modern cities and an important demand response source for power systems.In order to enhance its energy efficiency and utilize its flexibility,strategic operation is indispensable.However,finding an optimal policy for DCS operation is a challenging task because of the high inter-connectivity among components.The evolution of cooling load uncertainties further increases the difficulties.This paper addresses the aforementioned challenges by proposing a novel optimal power dispatch model for DCS.The proposed model optimizes water temperature and mass flow rates simultaneously to improve the energy efficiency as much as possible.It also explicitly describes the uncertainty accumulation and propagation.Chance-constrained programming is employed to guarantee the cooling service quality.We further propose a more timeefficient formulation to overcome the computational intractability caused by the non-smooth and non-convex constraints.Numerical experiments based on a real DCS confirm that a time-efficient formulation can save about half of solution time with negligible cost increase.

Influences of uncertainties to the generation feasible region for medium- and long-term electricity transaction

For the implementation of power market in China,medium-and Iong-term security checks are essential for bilateral transactions,of which the electricity quantity that constitutes the generation feasible region(GFR)is the target.However,uncertainties from load forecasting errors and transmission contingencies are threats to medium-and Iong-term electricity tradi ng in terms of their in flue nces on the GFR.In this paper,we prese nt a graphic distortio n pattern in a typical threegenerator system using the Monte Carlo method and projection theory based on security constrained economic dispatch.The underlying potential risk to GFR from uncertainties is clearly visualized,and their impact characteristics are discussed.A case study on detailed GFR distortion was included to dem on strate the effectiveness of this visualization model.The result implies that a small uncertainty could distort the GFR to a remarkable extent and that different line-contingency precipitates disparate the GFR distortion patterns,thereby eliciting great emphasis on load forecasting and line reliability in electricity transacti ons.