Research on Reduction of Solar Power Curtailment with Grid Connected Energy Storage System Based on Time-Series Production Simulation

Due to the variable output of renewable energy (RE) generation, difficulties of dispatching RE for power system operators could not be avoided. One of possible solutions is the energy storage technology, especially the battery storage system. The large-scale energy storage system is available to support power system reliable flexibility for load following and system frequency regulation. In this paper, the bottlenecks of large-scale solar power generation dispatching and operation in Qinghai grid are discussed, and a new PV-energy storage coordinated dispatching method is proposed for reduction of PV curtailment in Qinghai. Moreover, the validation based on the time-series production simulation is provided using real data from Qinghai. The results indicate that the proposed method can effectively decrease the curtailment of solar power and future vision of large-scale solar power coordinated operation with energy storage system is also presented.

Investigating Practical Measures to Reduce Power Outages and Energy Curtailments

This work attempts to investigate some practical measures that may reduce severe power outages that lead to energy curtailments. The first step of this attempt is to explore, from the consumer’s perspective, the adverse effects of the energy curtailments that reflect enormous damages (tangible and intangible) to the residential sector in the city of Riyadh (the capital of the Kingdom of Saudi Arabia). The second step is to propose, analyze, and employ energy conservation strategies that lead to both energy conservation and costs savings. The study results show that some customers will suffer enormous tangible and intangible losses should these outages occur during specific times, seasons, and for prolonged durations. In order to reduce these power outages and hence mitigate their adverse effects and consequences, the study proposes proper practical measures and solutions without compromising the consumers’ needs, satisfaction, and convenience.

Optimal Scheduling Strategy of Source-Load-Storage Based onWind Power Absorption Benefit

In recent years,the proportion of installed wind power in the three north regions where wind power bases are concentrated is increasing,but the peak regulation capacity of the power grid in the three north regions of China is limited,resulting in insufficient local wind power consumption capacity.Therefore,this paper proposes a two-layer optimal scheduling strategy based on wind power consumption benefits to improve the power grid’s wind power consumption capacity.The objective of the uppermodel is tominimize the peak-valley difference of the systemload,which ismainly to optimize the system load by using the demand response resources,and to reduce the peak-valley difference of the system load to improve the peak load regulation capacity of the grid.The lower scheduling model is aimed at maximizing the system operation benefit,and the scheduling model is selected based on the rolling schedulingmethod.The load-side schedulingmodel needs to reallocate the absorbed wind power according to the response speed,absorption benefit,and curtailment penalty cost of the two DR dispatching resources.Finally,the measured data of a power grid are simulated by MATLAB,and the results show that:the proposed strategy can improve the power grid’s wind power consumption capacity and get a large wind power consumption benefit.

Optimal Trade-Off Between Regulation and Wind Curtailment in the Economic Dispatch Problem

Generator regulation and wind power curtailment are two conflicting ways to deal with the inaccurate prediction and volatility of wind power.This study focuses on planning the day-ahead schedule based on optimal trade-off between regulation of generators and wind curtailment.Compared to traditional economic dispatch methods,the proposed schedule is more robust and adaptive to multiple forecast scenarios instead of a single forecast scenario.The works of this paper are as follows:First,an economic dispatch problem based on multiple scenarios is formulated with the objective of minimizing both generator regulation and wind curtailment.Next,a forecast method for wind power scenarios is given.Finally,the proposed model is verified by comparing with other dispatch models that are based on single forecast scenario.The simulation results demonstrate the effectiveness of the proposed method with less wind curtailment,generator regulation,and operational cost.In addition,the penalty factors are set as parameters and the influences on the generators’regulation and wind curtailment are analyzed,providing the reference for system operators with different regulatory purposes.

Mode for reducing wind curtailment based on battery transportation

Renewable energy sources, such as wind and solar, face several obstacles, including curtailment, where the generated energy exceeds local demand and production must be reduced because of limited transmission capacity.Simultaneously, consumer demand for large-capacity batteries is expanding given the recent rapid development of electric vehicles(EVs) and plug-in hybrid EVs. A batterycharging mode, in which discharged batteries are transported from battery exchange stations in high-load areas to wind farms, is proposed to alleviate curtailment. In this study, batteries store otherwise curtailed energy and smooth the wind power output simultaneously. The structure of the battery-charging device is discussed, and the concept of a battery-charging container is proposed. The control principle of the battery-charging management unit is presented with a simulation model constructed in the PSCAD/EMTDC environment. A case study is simulated,and the feasibility of the mode is analyzed considering the levelized cost and energy losses. Simulation results show that this mode is a feasible solution to alleviating wind curtailment and providing fresh impetus for developing EV battery exchanges.

Utilization of cross-regional interconnector and pumped hydro energy storage for further introduction of solar PV in Japan

The amount of solar PV installed capacity has steadily increased to 44.5 GW at the end of FY2017,since the introduction of the Feed in Tariff(FiT)to Japan in 2012.On the other hand,since the first curtailment of solar PV was conducted on October 13th,2018 in the Kyushu area,the curtailment has been frequently executed including wind power after that.In this study,cross-regional interconnector and pumped hydro energy storage(PHES)are focused on mitigating curtailment.In Japan,there are 9 electric power areas which connected each other by cross-regional interconnectors.According to the historical operation,cross-regional interconnectors were secured as emergency flexible measures,but after the implicit auction was started from October 2018,it is used on merit order.Regarding a PHES in Japan,they have been built with nuclear power plants for several decades.Because the output of nuclear power generation is constant,so the PHES is used to absorb the surplus at nighttime when the demand declines.All nuclear power plants in Japan have been shut down after the accident at the Fukushima Daiichi Nuclear Power Plant following the Great East Japan Earthquake that occurred on March 11th,2011.There are several nuclear power plants that have been restarted(9 reactors,as of August 2019).In this study,the amount of curtailment for solar PV in the Kyushu area is sent to the Chugoku area using the cross-regional interconnector(Kanmon line).Then,the PHES in the Chugoku area is pumping with low price.Because the spot price in the market is low when the curtailment is executed.After that,the PHES is generating at night with high price when the solar PV is not generating.It makes a profit by the deference for the cost of pumping and the revenue of generating by the PHES.As a calculation result,for one week from May 2nd to 8th,2019,a profit becomes 152.2 million JPY(about 1.22 million EUR).For this purpose,it is necessary to raise the operation capacity of the cross-regional interconnector up to the rated capacity with the frequency control function of solar PV instead of the capacity to keep frequency in the event of an accident.This will allow the further introduction of solar PV in Japan.

Oil and Gas Policies in the World’s Major Countries

Revolutionary adjustments were made to the global energy structure in 2017.For the first time,OPEC cooperated with non-OPEC countries to reach an agreement on production curtailment in order to stabilize the oil and gas market.On the one hand,host countries have introduced various fiscal and tax incentive policies to attract investment in the oil and gas industry;on the other,the lack of fiscal revenue has been made up for and the low-carbon environmental protection policy supported by imposing a value-added tax and environmental protection tax.Major consumers have introduced a timetable for the sales prohibition of fuel vehicles and supported the development of new energy vehicles with fiscal and taxation policies.However,from the perspective of the full life cycle,the promotion of electric vehicles is only conducive to partially improving the environment and is not the optimal choice for national energy structure optimization.Electric vehicles will have real significance for environmental protection only after clean electric power is solved.As such,implementing the sales prohibition of fuel vehicles with administrative power goes against market discipline.

A New Model for Transmission Network Expansion and Reactive Power Planning in a Deregulated Environment

Transmission network expansion planning (TNEP) is a challenging issue especially in new restructured electricity mar-kets environment. TNEP can be incorporated with reactive power planning in which the operating conditions will be satisfied. In this paper a combinatorial mathematical model has been presented to solve transmission expansion and reactive power planning problem (TEPRPP) simultaneously. The proposed model is a non-convex problem having a mixed integer nonlinear nature where the number of candidate solutions to be evaluated increases exponentially according to the system size. The objective function of TEPRPP comprises the new circuits’ investment and production costs as well as load curtailment penalty payments. A real genetic algorithm (RGA) aimed to obtaining a significant quality solution to handle such a complicated problem has been employed. An interior point method (IPM) is applied to solve the proposed concurrent optimization problem in the solution steps of TEPRPP model. This paper proposes a new methodology for the best location as well as the capacity of VAr sources;it is tested on two well-known systems;the Garver and IEEE 24-bus systems. The obtained results show the capability and the viability of the proposed TEPRPP model incorporating operating conditions.

A Mixed-integer Linear Programming Model for Defining Customer Export Limit in PV-rich Low-voltage Distribution Networks

A photovoltaic(PV)-rich low-voltage(LV)distribution network poses a limit on the export power of PVs due to the voltage magnitude constraints.By defining a customer export limit,switching off the PV inverters can be avoided,and thus reducing power curtailment.Based on this,this paper proposes a mixed-integer nonlinear programming(MINLP)model to define such optimal customer export.The MINLP model aims to minimize the total PV power curtailment while considering the technical operation of the distribution network.First,a nonlinear mathematical formulation is presented.Then,a new set of linearizations approximating the Euclidean norm is introduced to turn the MINLP model into an MILP formulation that can be solved with reasonable computational effort.An extension to consider multiple stochastic scenarios is also presented.The proposed model has been tested in a real LV distribution network using smart meter measurements and irradiance profiles from a case study in the Netherlands.To assess the quality of the solution provided by the proposed MILP model,Monte Carlo simulations are executed in OpenDSS,while an error assessment between the original MINLP and the approximated MILP model has been conducted.

Deep reinforcement learning for wind and energy storage coordination in wholesale energy and ancillary service markets

Wind energy has been increasingly adopted to mitigate climate change.However,the variability of wind energy causes wind curtailment,resulting in considerable economic losses for wind farm owners.Wind curtailment can be reduced using battery energy storage systems(BESS)as onsite backup sources.Yet,this auxiliary role may significantly weaken the economic potential of BESS in energy trading.Ideal BESS scheduling should balance onsite wind curtailment reduction and market bidding,but practical implementation is challenging due to coordination complexity and the stochastic nature of energy prices and wind generation.We investigate the joint-market bidding strategy of a co-located wind-battery system in the spot and Regulation Frequency Control Ancillary Service markets.We propose a novel deep reinforcement learning-based approach that decouples the system’s market participation into two related Markov decision processes for each facility,enabling the BESS to absorb onsite wind curtailment while performing joint-market bidding to maximize overall operational revenues.Using realistic wind farm data,we validated the coordinated bidding strategy,with outcomes surpassing the optimization-based benchmark in terms of higher revenue by approximately 25%and more wind curtailment reduction by 2.3 times.Our results show that joint-market bidding can significantly improve the financial performance of wind-battery systems compared to participating in each market separately.Simulations also show that using curtailed wind generation as a power source for charging the BESS can lead to additional financial gains.The successful implementation of our algorithm would encourage co-location of generation and storage assets to unlock wider system benefits.

Multi-energy Storage System Model Based on Electricity Heat and Hydrogen Coordinated Optimization for Power Grid Flexibility

Based on decreasing the flexibility of the power grid through the integration of large-scale renewable energy,a multi-energy storage system architectural model and its coor-dination operational strategy with the same flexibility as in the pumped storage power station and battery energy storage system(BESS)are studied.According to the new energy fluctuation characteristics and the different peak valley parameters in the power grid,this paper proposes a electricity heat hydrogen multi-energy storage system(EHH-MESS)and its coordination and optimization operational model to reduce the curtailment of wind power and photovoltaic(PV)to the power grid and improve the flexibility of the power grid.Finally,this paper studied the simulation model of an energy storage optimization control strategy after the multi-energy storage system is connected to the distribution networks,and analyzed three operational modes of the multi-energy storage system.The simulation results show that the EHH-MESS proposed in this paper has a better power grid regulation flexibility and economy,and can be used to replace the battery energy storage system based on MATLAB.

Accommodation of curtailed wind power by electric water heaters based on a new hybrid prediction approach

Wind power curtailment is of great importance with the increase of large-scale wind power connected to the grid. A new concept of redundant wind power accommodated by dispatching electric water heaters(EWHs) is developed in the paper. Precise predictions of wind power and EWHs load power are the basis for this work. A hybrid multi-kernel prediction approach integrating an adaptive fruit fly optimization algorithm(AFOA)and multi-kernel relevance vector machine(MKRVM) is proposed to deal with the sample distribution of multisource heterogeneous features uncovered by an energy entropy method, where AFOA is used to determine the kernel parameters in MKRVM adaptively and avoid the arbitrariness. For the large computation of the prediction approach, parallel computation based on the Hadoop cluster is used to accelerate the calculation. Then, an economic dispatching model for accommodating wind power is built taking into account the penalty of curtailed wind power and the operation cost of EWHs. The proposedscheme is implemented in an intelligent residential district.The results show that the optimization performance of the hybrid prediction approach is superior to those of four usual optimization algorithms in this case. Regular or orderly scheduling of EWHs enables accommodation of superfluous wind power and reduces dispatch cost.

Benefit Evaluation of the Deep Peak-regulation Market in the Northeast China Grid

In the Northeast China Grid(NCG),the percentage of wind power has reached nearly 20%of the total installed generation capacity,which causes increasing demands for deep peak-regulation capacity(DPC)during the operation of power systems.The shortage of DPC has become a significant problem in the NCG which may lead to wind curtailments and affect the security of power systems as well as the heating needs for inhabitants.In order to cope with this DPC shortage issue,the deep peak-regulation market(DPM)was established and has been running steadily in the past few years in NCG.This paper elaborates on the roles of the market players and the operational processes of the DPM,of which the advancements in terms of management mechanism are summarized.Moreover,benefits of the DPM for social harmony,environmental protection and economic efficiency are analyzed,for which relevant evaluation indices are proposed.A five-unit simulation system is constructed to illustrate the operation and benefits of the DPM.And focusing on comparisons with the previous Two Rules,the case study of Liaoning Power Grid verifies further that the DPM is feasible and able to bring more benefits to grids.

Short-term Transmission Maintenance Scheduling Considering Network Topology Optimization

With the increasing penetration of renewable energy sources,transmission maintenance scheduling(TMS)will have a larger impact on the accommodation of wind power.Meanwhile,the more flexible transmission network topology owing to the network topology optimization(NTO)technique can ensure the secure and economic operation of power systems.This paper proposes a TMS model considering NTO to decrease the wind curtailment without adding control devices.The problem is formulated as a two-stage stochastic mixed-integer programming model.The first stage arranges the maintenance periods of transmission lines.The second stage optimizes the transmission network topology to minimize the maintenance cost and system operation in different wind speed scenarios.The proposed model cannot be solved efficiently with off-theshelf solvers due to the binary variables in both stages.Therefore,the progressive hedging algorithm is applied.The results on the modified IEEE RTS-79 system show that the proposed method can reduce the negative impact of transmission maintenance on wind accommodation by 65.49%,which proves its effectiveness.

Autonomous Charging of Electric Vehicle Fleets to Enhance Renewable Generation Dispatchability

A total of 19%of generation capacity in California is offered by PV units and over some months,more than 10%of this energy is curtailed.In this research,a novel approach to reducing renewable generation curtailment and increasing system flexibility by means of electric vehicles'charging coordination is presented.The presented problem is a sequential decision making process,and is solved by a fitted Q-iteration algorithm which unlike other reinforcement learning methods,needs fewer episodes of learning.Three case studies are presented to validate the effectiveness of the proposed approach.These cases include aggregator load following,ramp service and utilization of non-deterministic PV generation.The results suggest that through this framework,EVs successfully learn how to adjust their charging schedule in stochastic scenarios where their trip times,as well as solar power generation are unknown beforehand.

Water-filling algorithm based approach for management of responsive residential loads

Integration of large number of electric vehicles(EVs)with distribution networks is devastating for conventional power system devices such as transformers and power lines etc.This paper proposes a methodology for management of responsive household appliances management and EVs with water-filling algorithm.With the proposed scheme,the load profile of a transformer is retained below its rated capacity while minimally affecting the associated consumers.When the instantaneous demand at transformer increases beyond its capacity,the proposed methodology dynamically allocates demand curtailment limit(DCL)to each home served by transformer.The DCL allocation takes convenience factors,load profile and information of flexible appliances into account to assure the comfort of all the consumers.The proposed scheme is verified by modeling and simulating five houses and a distribution transformer.The smart appliances such as an HVAC,a water heater,a cloth dryer and an EV are also modeled for the study.Results show that the proposed scheme performs to reduce overloading effects of the transformer efficiently and assures comfort of the consumers at the same time.

Optimal Energy Reserve Scheduling in Integrated Electricity and Gas Systems Considering Reliability Requirements

With the growing interdependence between the electricity system and the natural gas system,the operation uncertainties in either subsystem,such as wind fluctuations or component failures,could have a magnified impact on the reliability of the whole system due to energy interactions.A joint reserve scheduling model considering the cross-sectorial impacts of operation uncertainties is essential but still insufficient to guarantee the reliable operation of the integrated electricity and natural gas system(IEGS).Therefore,this paper proposes a day-ahead security-constrained unit commitment(SCUC)model for the IEGS to schedule the operation and reserve simultaneously considering reliability requirements.Firstly,the multi-state models for generating units and gas wells are established.Based on the multi-state models,the expected unserved energy cost(EUEC)and the expected wind curtailment cost(EWC)criteria are proposed based on probabilistic methods considering wind fluctuation and random failures of components in IEGS.Furthermore,the EUEC and EWC criteria are incorporated into the day-ahead SCUC model,which is nonconvex and mathematically reformulated into a solvable mixed-integer second-order cone programming(MISOCP)problem.The proposed model is validated using an IEEE 30-bus system and Belgium 20-node natural gas system.Numerical results demonstrate that the proposed model can effectively schedule the energy reserve to guarantee the reliable operation of the IEGS considering the multiple uncertainties in different subsystems and the cross-sectorial failure propagation.

Sizing and Siting of Battery Energy Storage Systems:A Colombian Case

This paper presents a mixed-integer linear programming(MILP)formulation for sizing and siting of battery energy storage systems(BESSs).The problem formulation seeks to minimize both operation costs and BESS investment.The proposed model includes restrictions of the conventional securityconstrained unit commitment problem,a piece-wise linear approximation to model power losses,and a linear model of hydro generation units.The proposed model is tested in a 6-bus test system and a 15-bus system representing the Colombian power system.For the two studied systems,simulation results show that the reduction of operation costs due to the installation of BESSs compensates the investments,under some of the considered technical cost cases.Additionally,results show that adequate sizing and siting of BESSs reduce renewable energy curtailment in the Colombian power system with high penetration of fluctuating renewable generation.