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 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.

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.

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.

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.

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.