Rolling Decision Model of Thermal Power Retrofit and Generation Expansion Planning Considering Carbon Emissions and Power Balance Risk

With the increasing urgency of the carbon emission reduction task,the generation expansion planning process needs to add carbon emission risk constraints,in addition to considering the level of power adequacy.However,methods for quantifying and assessing carbon emissions and operational risks are lacking.It results in excessive carbon emissions and frequent load-shedding on some days,although meeting annual carbon emission reduction targets.First,in response to the above problems,carbon emission and power balance risk assessment indicators and assessment methods,were proposed to quantify electricity abundance and carbon emission risk level of power planning scenarios,considering power supply regulation and renewable energy fluctuation characteristics.Secondly,building on traditional two-tier models for low-carbon power planning,including investment decisions and operational simulations,considering carbon emissions and power balance risks in lower-tier operational simulations,a two-tier rolling model for thermal power retrofit and generation expansion planning was established.The model includes an investment tier and operation assessment tier and makes year-by-year decisions on the number of thermal power units to be retrofitted and the type and capacity of units to be commissioned.Finally,the rationality and validity of the model were verified through an example analysis,a small-scale power supply system in a certain region is taken as an example.The model can significantly reduce the number of days of carbon emissions risk and ensure that the power balance risk is within the safe limit.

Power generation expansion planning approach considering carbon emission constraints

Decarbonization of the power sector in China is an essential aspect of the energy transition process to achieve carbon neutrality.The power sector accounts for approximately 40%of China’s total CO_(2) emissions.Accordingly,collaborative optimization in power generation expansion planning(GEP)simultaneously considering economic,environmental,and technological concerns as carbon emissions is necessary.This paper proposes a collaborative mixedinteger linear programming optimization approach for GEP.This minimizes the power system’s operating cost to resolve emission concerns considering energy development strategies,flexible generation,and resource limitations constraints.This research further analyzes the advantages and disadvantages of current GEP techniques.Results show that the main determinants of new investment decisions are carbon emissions,reserve margins,resource availability,fuel consumption,and fuel price.The proposed optimization method is simulated and validated based on China’s power system data.Finally,this study provides policy recommendations on the flexible management of traditional power sources,the market-oriented mechanism of new energy sources,and the integration of new technology to support the attainment of carbon-neutral targets in the current energy transition process.

Generation-expansion planning with linearized primary frequency response constraints

As renewable energy resources increasingly penetrate the electric grid,the inertia capability of power systems has become a developmental bottleneck.Nevertheless,the importance of primary frequency response(PFR)when making generation-expansion plans has been largely ignored.In this paper,we propose an optimal generation-expansion planning framework for wind and thermal power plants that takes PFR into account.The model is based on the frequency equivalent model.It includes investment,startup/shutdown,and typical operating costs for both thermal and renewable generators.The linearization constraints of PFR are derived theoretically.Case studies based on the modified IEEE 39-bus system demonstrate the efficiency and effectiveness of the proposed method.Compared with methods that ignore PFR,the method proposed in this paper can effectively reduce the cost of the entire planning and operation cycle,improving the accommodation rate of renewable energy.

Generation expansion planning considering efficient linear EENS formulation

Power system equipment outages are one of the most important factors affecting the reliability and economy of power systems.It is crucial to consider the reliability of the planning problems.In this paper,a generation expansion planning(GEP)model is proposed,in which the candidate generating units and energy storage systems(ESSs)are simultaneously planned by minimizing the cost incurred on investment,operation,reserve,and reliability.The reliability cost is computed by multiplying the value of lost load(VOLL)with the expected energy not supplied(EENS),and this model makes a compromise between economy and reliability.Because the computation of EENS makes the major computation impediment of the entire model,a new efficient linear EENS formulation is proposed and applied in a multi-step GEP model.By doing so,the computation efficiency is significantly improved,and the solution accuracy is still desirable.The proposed GEP model is illustrated using the IEEE-RTS system to validate the effectiveness and superiority of the new model.

Study on Long-Term Generation Expansion Planning upon the LNG Price Fluctuations

About 37% of South Korea’s greenhouse gas emission is from electricity generation. Most of the country’s electric power is fundamentally generated by nuclear, thermal and LNG facilities. And LNG, of them, is characterized to require high cost for power generation but CO2 coefficient is lower than thermal generation. Amid the ongoing global efforts to tackle global warming, shale gas introduction and changing global environment, LNG prices are expected to fluctuate. Against this backdrop, this paper seeks to perform scenario tests on LNG fuel cost fluctuation and examine its long-term effects on generation expansion planning.

Integrated generation-transmission expansion planning for offshore oilfield power systems based on genetic Tabu hybrid algorithm

To address the planning issue of offshore oil-field power systems, an integrated generation-transmission expansion planning model is proposed. The outage cost is considered and the genetic Tabu hybrid algorithm(GTHA)is developed to find the optimal solution. With the proposed integrated model, the planning of generators and transmission lines can be worked out simultaneously,which outweighs the disadvantages of separate planning,for instance, unable to consider the influence of power grid during the planning of generation, or insufficient to plan the transmission system without enough information of generation. The integrated planning model takes into account both the outage cost and the shipping cost, which makes the model more practical for offshore oilfield power systems. The planning problem formulated based on the proposed model is a mixed integer nonlinear programming problem of very high computational complexity, which is difficult to solve by regular mathematical methods. A comprehensive optimization method based on GTHA is also developed to search the best solution efficiently.Finally, a case study on the planning of a 50-bus offshore oilfield power system is conducted, and the obtained results fully demonstrate the effectiveness of the presented model and method.

Analysis of electric vehicle charging using the traditional generation expansion planning analysis tool WASP-IV

Electric vehicles(EV)are proposed as a measure to reduce greenhouse gas emissions in transport and support increased wind power penetration across modern power systems.Optimal benefits can only be achieved,if EVs are deployed effectively,so that the exhaust emissions are not substituted by additional emissions in the electricity sector,which can be implemented using Smart Grid controls.This research presents the results of an EV roll-out in the all island grid(AIG)in Ireland using the long term generation expansion planning model called the Wien Automatic System Planning IV(WASP-IV)tool to measure carbon dioxide emissions and changes in total energy.The model incorporates all generators and operational requirements while meeting environmental emissions,fuel availability and generator operational and maintenance constraints to optimize economic dispatch and unit commitment power dispatch.In the study three distinct scenarios are investigated base case,peak and off-peak charging to simulate the impacts of EV’s in the AIG up to 2025.

Distributionally Robust Co-optimization of Transmission Network Expansion Planning and Penetration Level of Renewable Generation

Transmission network expansion can significantly improve the penetration level of renewable generation.However,existing studies have not explicitly revealed and quantified the trade-off between the investment cost and penetration level of renewable generation.This paper proposes a distributionally robust optimization model to minimize the cost of transmission network expansion under uncertainty and maximize the penetration level of renewable generation.The proposed model includes distributionally robust joint chance constraints,which maximize the minimum expectation of the renewable utilization probability among a set of certain probability distributions within an ambiguity set.The proposed formulation yields a twostage robust optimization model with variable bounds of the uncertain sets,which is hard to solve.By applying the affine decision rule,second-order conic reformulation,and duality,we reformulate it into a single-stage standard robust optimization model and solve it efficiently via commercial solvers.Case studies are carried on the Garver 6-bus and IEEE 118-bus systems to illustrate the validity of the proposed method.

Transmission Network Expansion Planning Considering Uncertainties in Loads and Renewable Energy Resources

This paper proposes a novel method for transmission network expansion planning(TNEP)that take into account uncertainties in loads and renewable energy resources.The goal of TNEP is to minimize the expansion cost of candidate lines without any load curtailment.A robust linear optimization algorithm is adopted to minimize the load curtailment with uncertainties considered under feasible expansion costs.Hence,the optimal planning scheme obtained through an iterative process would be to serve loads and provide a sufficient margin for renewable energy integration.In this paper,two uncertainty budget parameters are introduced in the optimization process to limit the considered variation ranges for both the load and the renewable generation.Simulation results obtained from two test systems indicate that the uncertainty budget parameters used to describe uncertainties are essential to arrive at a compromise for the robustness and optimality,and hence,offer a range of preferences to power system planners and decision makers.

A Scenario-Based Robust Transmission Network Expansion Planning Method for Consideration of Wind Power Uncertainties

This paper uses a novel scenario generation method for tackling the uncertainties of wind power in the transmission network expansion planning(TNEP)problem.A heuristic moment matching(HMM)method is first applied to generate the typical scenarios for capturing the stochastic features of wind power,including expectation,standard deviation,skewness,kurtosis,and correlation of multiple wind farms.Then,based on the typical scenarios,a robust TNEP problem is presented and formulated.The solution of the problem is robust against all the scenarios that represent the stochastic features of wind power.Three test systems are used to verify the HMM method and is compared against Taguchi’s Orthogonal Array(OA)method.The simulation results show that the HMM method has better performance than the OA method in terms of the trade-off between robustness and economy.Additionally,the main factors influencing the planning scheme are studied,including the number of scenarios,wind farm capacity,and penalty factors,which provide a reference for system operators choosing parameters.

Expansion Planning for Renewable Integration in Power System of Regions with Very High Solar Irradiation

In this paper,we address the long-term generation and transmission expansion planning for power systems of regions with very high solar irradiation.We target the power systems that currently rely mainly on thermal generators and that aim to adopt high shares of renewable sources.We propose a stochastic programming model with expansion alternatives including transmission lines,solar power plants(photovoltaic and concentrated solar),wind farms,energy storage,and flexible combined cycle gas turbines.The model represents the longterm uncertainty to characterize the demand growth,and the short-term uncertainty to characterize daily solar,wind,and demand patterns.We use the Saudi Arabian power system to illustrate the functioning of the proposed model for several cases with different renewable integration targets.The results show that a strong dependence on solar power for high shares of renewable sources requires high generation capacity and storage to meet the night demand.

可再生能源激励制度下的低碳经济电源规划

可再生能源发电技术的应用会对CO2减排产生积极作用,然而由于其不够成熟,因此出现高投资成本和低投资回报率的问题。为鼓励可再生能源发电技术的发展,电力行业提出了多种激励措施。将绿色证书交易机制和碳交易机制引入到电源规划模型中,考虑规划期内系统收益、投资成本以及运行成本,建立了以规划期内系统净收益最大为目标的低碳经济电源规划模型,在传统约束条件的基础上,增加了可再生能源配额、出售或购买绿色证书数量和碳交易量等约束条件。利用离散细菌群体趋药性算法(discrete bacterial colony chemotaxis algorithm,DBCC)进行优化求解,对低碳经济电源规划与传统电源规划优化结果进行了对比分析,并在不同碳税水平、不同碳排放限额、不同可再生能源配额比例3种情况下进行了灵敏度分析。算例结果验证了上述模型和算法的合理性。

考虑碳排放权分配的低碳电源规划

在低碳经济环境下,电力行业实行CO2减排势在必行。而低碳环境下的电源规划则是实现CO2减排的关键之一。"总量管制与排放交易"是公认的实现CO2减排及获得低碳效益的有效措施。而推行该措施的关键在于碳排放权的分配方式。文中从低碳政策、低碳技术和低碳市场3个方面分析了低碳因素对电源规划的影响;提出了基于区域比较的碳排放权分配机制,并将低碳因素引入传统的电源规划模型,建立了低碳电源规划模型;应用改进的基于差分进化的粒子群优化算法求解低碳电源规划问题,并通过仿真算例对所建立的规划模型进行了验证。仿真结果表明了所建模型的正确性和适用性,为当前的电源规划工作提供了有益的参考。

结合独立发电商和环境保护的电源规划模型

给出了电力市场环境下的电源规划模型,充分考虑了两者对电源规划工作的影响.为了从根本上克服“组合爆炸”问题,结合电源规划具体工程实际,提出了一种实用且快速的电源规划算法,以电源投资费用等年值最小为目标函数,结合了相关技术约束(水火电平衡、电力平衡、电源投产/电厂开工时间)条件.通过对一个实际系统的测试,表明提出的模型和算法是可行、有效的.

计及碳交易成本及能效电厂的电源规划模型

在低碳经济背景下,新能源机组及碳交易机制的引入成为电力系统实现低碳减排重要的方法。同时,由于需求侧资源在降低系统碳排放量和增加系统可靠性方面有较大潜力,为此,将需求侧的能效电厂加入到规划方案中,建立了包含系统经济性及可靠性的低碳电源规划模型,目标函数中计及了机组建设成本、能效电厂建设成本、机组运行成本、碳交易成本,并将可靠性约束条件加入到模型中。采用离散细菌群体趋药性算法对所建模型进行求解,对两种电源规划模型进行了对比,并在不同碳排放限额、不同电量不足期望值限额两种情况下进行了灵敏度分析,仿真结果验证了模型和算法的有效性。

可再生能源激励制度下多场景灵活电源规划

分析了影响电源规划的不确定因素,引入适应成本的概念,建立了灵活电源规划模型。通过指数模型和线性模型描述不确定因素的变化规律,利用场景方法处理电源规划中的不确定因素。为了减少文中的计算量,采用k-means聚类算法缩减场景数目。利用离散细菌群体趋药性算法对所建规划模型进行优化求解。定义了灵活度的概念以便分析方案的灵活程度。通过算例验证所提出模型与算法的合理性,实现了电力行业可持续性和经济性的协调发展,对低碳经济灵活电源规划具有一定的指导作用。

电源规划模型及求解方法研究综述

对传统的电源规划模型结合需求侧管理、考虑环境保护和电力市场环境下的电源规划模型进行了详细的研究,总结了实施需求侧管理、环境保护对电源规划的影响和电力市场改革对电源规划的新要求,分析了各种模型中电源规划目标函数和考虑约束条件的不同。然后将电源规划问题所采用的求解方法主要分为数学优化方法和人工智能方法进行归纳,包括动态优化法、混合整数规划法、专家系统、模糊理论、遗传算法和人工神经网络等,对比了各种方法的优缺点。最后阐述了当前电源规划过程中尚待深入研究的问题。

低碳经济下的电源规划研究

从政策因素、技术因素和市场因素3方面探讨了碳经济对电源规划的影响,建立了评估电源规划的碳经济指标;并将该指标引入传统的电源规划模型,建立了低碳经济下的电源规划模型。通过仿真研究,分析了碳经济指标引入前后的电源规划方案。仿真结果表明,低碳经济下的电源规划能够有效引导能源结构调整,实现CO_2减排,符合当今能源发展趋势和低碳经济要求。

电源规划研究综述

从电源规划模型和优化算法两方面进行研究。在建模方面,根据规划指标和约束条件的不同将电源规划模型分为四大类进行探讨:传统的电源规划模型及其改进、考虑需求侧管理的电源规划模型、电力市场环境下的电源规划模型,以及当前正在研究的基于低碳经济的电源规划模型;在算法方面,将优化算法分为分解协调式算法、启发式算法、数学优化算法和智能优化算法,并对各类算法中的典型算法进行了深入的分析。

Modeling optimal long-term investment strategies of hybrid wind-thermal companies in restructured power market

In this paper,a novel framework for the estimation of optimal investment strategies for combined wind-thermal companies is proposed.The medium-term restructured power market was simulated by considering the stochastic and rational uncertainties,the wind uncertainty was evaluated based on a data mining technique,and the electricity demand and fuel price were simulated using the Monte Carlo method.The Cournot game concept was used to determine the Nash equilibrium for each state and stage of the stochastic dynamic programming(DP).Furthermore,the long-term stochastic uncertainties were modeled based on the Markov chain process.The longterm optimal investment strategies were then solved for combined wind-thermal investors based on the semi-definite programming(SDP)technique.Finally,the proposed framework was implemented in the hypothetical restructured power market using the IEEE reliability test system(RTS).The conducted case study confirmed that this framework provides robust decisions and precise information about the restructured power market for combined wind-thermal investors.