A robust optimization model for demand response management with source-grid-load collaboration to consume wind-power

To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.

Overview of the Global Electricity System in Oman Considering Energy Demand Model Forecast

Lately,in modern smart power grids,energy demand for accurate forecast of electricity is gaining attention,with increased interest of research.This is due to the fact that a good energy demand forecast would lead to proper responses for electricity demand.In addition,proper energy demand forecast would ensure efficient planning of the electricity industry and is critical in the scheduling of the power grid capacity and management of the entire power network.As most power systems are been deregulated and with the rapid introduction and development of smart-metering technologies in Oman,new opportunities may arise considering the efficiency and reliability of the power system;like price-based demand response programs.These programs could either be a large scale for household,commercial or industrial users.However,excellent demand forecasting models are crucial for the deployment of these smart metering in the power grid based on good knowledge of the electricity market structure.Consequently,in this paper,an overview of the Oman regulatory regime,financial mechanism,price control,and distribution system security standard were presented.More so,the energy demand forecast in Oman was analysed,using the econometric model to forecasts its energy peak demand.The energy econometric analysis in this study describes the relationship between the growth of historical electricity consumption and macro-economic parameters(by region,and by tariff),considering a case study of Mazoon Electricity Distribution Company(MZEC),which is one of the major power distribution companies in Oman,for effective energy demand in the power grid.

SmartMicro Grid Energy System Management Based on Optimum Running Cost for Rural Communities in Rwanda

The governmental electric utility and the private sector are joining hands to meet the target of electrifying all households by 2024.However,the aforementioned goal is challenged by households that are scattered in remote areas.So far,Solar Home Systems(SHS)have mostly been applied to increase electricity access in rural areas.SHSs have continuous constraints to meet electricity demands and cannot run income-generating activities.The current research presents the feasibility study of electrifying Remera village with the smart microgrid as a case study.The renewable energy resources available in Remera are the key sources of electricity in that village.The generation capacity is estimated based on the load profile.The microgrid configurations are simulated with HOMER,and the genetic algorithm is used to analyze the optimum cost.By analyzing the impact of operation and maintenance costs,the results show that the absence of subsidies increases the levelized cost of electricity(COE)five times greater than the electricity price from the public utility.The microgrid made up of PV,diesel generator,and batteries proved to be the most viable solution and ensured continuous power supply to customers.By considering the subsidies,COE reaches 0.186$/kWh,a competitive price with electricity from public utilities in Rwanda.

Agent-Based Simulation for Interconnection-Scale Renewable Integration and Demand Response Studies

This paper collects and synthesizes the technical requirements, implementation, and validation methods for quasi-steady agent-based simulations of interconnectionscale models with particular attention to the integration of renewable generation and controllable loads. Approaches for modeling aggregated controllable loads are presented and placed in the same control and economic modeling framework as generation resources for interconnection planning studies. Model performance is examined with system parameters that are typical for an interconnection approximately the size of the Western Electricity Coordinating Council(WECC) and a control area about 1/100 the size of the system. These results are used to demonstrate and validate the methods presented.

An Optimization Model of EVs Charging and Discharging for Power System Demand Leveling

This paper presents an advanced methodology for optimizing a UK network load demand with various uncertainties which are related to individual driving behaviours. Without the optimized regulation for traditional power system demand, EVs （electric vehicles） would have an adverse impact on the stability of power systems. This becomes more significant for large-scale EVs plugging into the power grid. Traditional optimized methodologies are effective only for EV charging. The proposed techniques improve the system flexibility and stability through an advanced optimization model and flexible bidirectional charging/discharging control. Three scenarios with different charging and discharging power levels and various penetration levels of EVs are discussed in detail in this paper. Simulation results demonstrate that bidirectional EV power flow control has vast potentials to improve the load demand profile, with increased proportion of EVs, and charging/discharging power levels.

Multi-objective Optimization of Integrated Energy Systems Considering Ladder-type Carbon Emission Trading and Refined Load Demand Response

In this paper,a novel multi-objective optimization model of integrated energy systems(IESs)is proposed based on the ladder-type carbon emission trading mechanism and refined load demand response strategies.First,the carbon emission trading mechanism is introduced into the optimal scheduling of IESs,and a ladder-type carbon emission cost calculation model based on rewards and penalties is established to strictly control the carbon emissions of the system.Then,according to different response characteristics of electric load and heating load,a refined load demand response model is built based on the price elasticity matrix and substitutability of energy supply mode.On these basis,a multi-objective optimization model of IESs is established,which aims to minimize the total operating cost and the renewable energy source(RES)curtailment.Finally,based on typical case studies,the simulation results show that the proposed model can effectively improve the economic benefits of IESs and the utilization efficiency of RESs.

Long-Term Electrical Load Forecasting in Rwanda Based on Support Vector Machine Enhanced with Q-SVM Optimization Kernel Function

In recent years, Rwanda’s rapid economic development has created the “Rwanda Africa Wonder”, but it has also led to a substantial increase in energy consumption with the ambitious goal of reaching universal access by 2024. Meanwhile, on the basis of the rapid and dynamic connection of new households, there is uncertainty about generating, importing, and exporting energy whichever imposes a significant barrier. Long-Term Load Forecasting (LTLF) will be a key to the country’s utility plan to examine the dynamic electrical load demand growth patterns and facilitate long-term planning for better and more accurate power system master plan expansion. However, a Support Vector Machine (SVM) for long-term electric load forecasting is presented in this paper for accurate load mix planning. Considering that an individual forecasting model usually cannot work properly for LTLF, a hybrid Q-SVM will be introduced to improve forecasting accuracy. Finally, effectively assess model performance and efficiency, error metrics, and model benchmark parameters there assessed. The case study demonstrates that the new strategy is quite useful to improve LTLF accuracy. The historical electric load data of Rwanda Energy Group (REG), a national utility company from 1998 to 2020 was used to test the forecast model. The simulation results demonstrate the proposed algorithm enhanced better forecasting accuracy.

Some Views about Recent Electric Power Supply Shortage in Shenzhen

Since the beginning of the year 2000, the power demands in Guangdong, Zhejiang provinces and Beijing Tianjin-Tangshan district have been increasing dramatically, power supply shortages have appeared again. This paper analyzes the reasons for the current power supply shortages in Shenzhen district and the problems existing presently in Shenzhen power system. It indicates that, to strengthen power demand forecast, to speed up power construction steps and with ’to develop power ahead of the rest’ as a fundamental target, are the precondition to the long term, steady development of power industry.

Inverse Proportion Technique Based Scheduling Strategy for Energy Storage System Considering Load Demand Differences

The energy storage system(ESS)as a demand-side management(DSM)resource can effectively smooth the load power fluctuation of a power system.However,designing a more reasonable ESS operational strategy will be a prerequisite before incorporating the energy storage device into DSM.As different load levels have different demands for the real-time chargedischarge power of an ESS,this paper proposes a heuristic ESS operation scheduling strategy which can take into account the electrical load demand differences.In this paper,firstly,two demand degree concepts for charging power and discharging power are defined to describe the differentiated ESS demand under the condition of different electrical load levels.Secondly,an inverse proportion technique based ESS scheduling strategy,with the consideration of the load demand difference,is proposed in this paper.Thirdly,some evaluating indices are defined in this paper for describing the influence of the proposed strategy on the smoothing degree of the daily load curve.Finally,several case studies are designed to verify the validity and correctness of the proposed technique,and the results show that the proposed technique can effectively smooth the load curve and improve the ability of peak shaving and valley filling.

Probabilistic steady-state and dynamic security assessment of power transmission system

Two-level system model based probabilistic steady-state and dynamic security assessment model is introduced in this paper.Uncertainties of nodal power injection caused by wind power and load demand,steady-state and dynamic security constraints and transitions between system configurations in terms of failure rate and repair rate are considered in the model.Time to insecurity is used as security index.The probability distribution of time to insecurity can be obtained by solving a linear vector differential equation.The coefficients of the differential equation are expressed in terms of configuration transition rates and security transition probabilities.The model is implemented in complex system successfully for the first time by using the following effective measures:firstly,calculating configuration transition rates effectively based on component state transition rate matrix and system configuration array;secondly,calculating the probability of random nodal power injection belonging to security region effectively according to practical parts of critical boundaries of security region represented by hyper-planes;thirdly,locating non-zero elements of coefficient matrix and then implementing sparse storage of coefficient matrix effectively;finally,calculating security region off-line for on-line use.Results of probabilistic security assessment can be used to conduct operators to analyze system security effectively and take preventive control.Test results on New England 10-generators and 39-buses power system verify the reasonableness and effectiveness of the method.

Generalized Predictive Control for Superheated Steam Temperature Regulation in a Supercritical Coal-fired Power Plant

The design and implementation of a Generalized Predictive Control(GPC)strategy for the superheated steam temperature regulation in a supercritical(SC)coal-fired power plant is presented.A Controlled Auto-Regressive MovingAverage(CARMA)model of the plant is derived from using the experimental data to approximately predict the plant’s future behavior.This model is required by the GPC algorithm to calculate the future control inputs.A new GPC controller is designed and its performance is tested through extensive simulation studies.Compared with the performance of the plant using a conventional PID controller,the steam temperature controlled by the GPC controller is found to be more stable.The stable steam temperature leads to more efficient plant operation and energy saving,as demonstrated by the simulation results.Plant performance improvement is also tested while the plant experiences the load demand changes and disturbances resulting from the malfunctioning of coal mills.

Optimal Operation of an Integrated Electricity-heat Energy System Considering Flexible Resources Dispatch for Renewable Integration

Large fluctuations may occur on the energy supply and the load sides when large-scale renewable energies are integrated,leading to great challenges in power systems.The renewable power curtailment is especially numerous in the integrated electricity-heat energy system(IEHES)on account of electricity-heat coupling.The flexible resources(FRs)on both the energy supply and load sides are introduced into the optimal dispatch of the IEHES and further modeled to alleviate the renewable fluctuations in this paper.On the energy supply side,three kinds of FRs based on electricity-heat coordination are modeled and discussed.On the load side,the shiftable electricity demand resource is characterized.On this basis,the solution for FRs participating in IEHES dispatch is given,with goals of maximizing the renewable penetration ratio and lowering operation costs.Two scenarios are performed,and the results indicate that the proposed optimal dispatch strategy can effectively reduce the renewable energy curtailment and improve the flexibility of the IEHES.The contribution degrees of different FRs for renewable integration are also explored.

Removal of veterinary antibiotics from anaerobically digested swine wastewater using an intermittently aerated sequencing batch reactor

A lab-scale intermittently aerated sequencing batch reactor（IASBR）was applied to treat anaerobically digested swine wastewater（ADSW）to explore the removal characteristics of veterinary antibiotics.The removal rates of 11 veterinary antibiotics in the reactor were investigated under different chemical organic demand（COD）volumetric loadings,solid retention times（SRT）and ratios of COD to total nitrogen（TN）or COD/TN.Both sludge sorption and biodegradation were found to be the major contributors to the removal of veterinary antibiotics.Mass balance analysis revealed that greater than 60%of antibiotics in the influent were biodegraded in the IASBR,whereas averagely 24%were adsorbed by sludge under the condition that sludge sorption gradually reached its equilibrium.Results showed that the removal of antibiotics was greatly influenced by chemical oxygen demand（COD）volumetric loadings,which could achieve up to 85.1%±1.4%at 0.17±0.041 kg COD/m-3/day,while dropped to 75.9%±1.3%and 49.3%±12.1%when COD volumetric loading increased to 0.65±0.032 and1.07±0.073 kg COD/m-3/day,respectively.Tetracyclines,the dominant antibiotics in ADSW,were removed by 87.9%in total at the lowest COD loading,of which 30.4%were contributed by sludge sorption and 57.5%by biodegradation,respectively.In contrast,sulfonamides were removed about 96.2%,almost by biodegradation.Long SRT seemed to have little obvious impact on antibiotics removal,while a shorter SRT of 30–40 day could reduce the accumulated amount of antibiotics and the balanced antibiotics sorption capacity of sludge.Influent COD/TN ratio was found not a key impact factor for veterinary antibiotics removal in this work.