Distributionally Robust Coordinated Expansion Planning for Generation, Transmission, and Demand Side Resources Considering the Benefits of Concentrating Solar Power Plants

Chen, Baorui; Liu, Tianqi; Li, Xuan; He, Chuan; Nan, Lu; Wu, Lei; Su, Xueneng

doi:10.1109/tpwrs.2022.3171515

Cited by 20 publications

(9 citation statements)

References 33 publications

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“…According to the International Energy Agency (IEA), electricity generated from variable renewable energy sources (VRES), such as wind and solar power, is projected to contribute to 80% of the global power generation increase over the next five years [1]. Although these VRES are greenhouse gas (GHG) free, their variability and intermittency pose significant short-term operational challenges for the generation system in balancing supply and demand [2,3]. This intermittency can occasionally lead to a reduction in the reliability of the generation system [4].…”

Section: A Motivationmentioning

confidence: 99%

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Enhancing Generation Expansion Planning With Integration of Variable Renewable Energy and Full-Year Hourly Multiple Load Levels Balance Constraints

Diewvilai,

Audomvongseree

2024

IEEE Access

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This paper proposes a method for generation expansion planning that incorporates full-year hourly multiple load levels balance constraints, providing sufficient flexibility to address load fluctuations and intermittency associated with variable renewable energy sources. Typically, ensuring that the generation system possesses enough flexibility to manage this intermittency involves considering the operational characteristics of generators within unit commitment constraints. However, to mitigate the substantial computational burden caused by the number and type of variables, various approximation techniques are often employed. Unfortunately, these techniques can introduce unrealistic elements into the problem. Instead of considering the operational characteristics of generators, this approach classifies the system's demand into three levels: base load, intermediate load, and peak load, using the proposed load classification method. The multiple load-level balance constraints are then applied to ensure that the capacity of generation units in each level is sufficient to meet their corresponding demand, with particular emphasis on matching fast-response generation units and their corresponding demand. The resulting generation expansion plan can be obtained with significantly reduced computational effort. The proposed load classification method and generation expansion planning approach have been tested using the latest power development plan of Thailand. Compared to another method that is not taken flexibility into account, 5 Gigawatts of fast-response generation capacity are selected instead of base load generation units. With the improved computational time achieved by the proposed generation expansion planning method, it can account for input data uncertainty by solving multiple generation expansion planning problems with varying input data and distinct individual probabilities. INDEX TERMS power generation planning, power generation reliability, generation expansion planning, renewable energy NOMENCLATURE A. ACRONYMS

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Section: A Motivationmentioning

confidence: 99%

“…The model used in the UC for must-take unit is defined in (1) as the product of the standard generation profile and maximum output capacity during the considering period. The model used in the reliability evaluation incorporates the forced outage rate (FOR) as shown in (2).…”

Section: ) Must-take Unitmentioning

confidence: 99%

Enhancing Generation Expansion Planning With Integration of Variable Renewable Energy and Full-Year Hourly Multiple Load Levels Balance Constraints

Diewvilai,

Audomvongseree

2024

IEEE Access

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“…Ref. [26] constructs a distributionally robust planning model considering load and wind stochastic behavior, while modeling demand response and solar power plants. A solution method to address the robust transmission planning is suggested in [27], where neither binary variables nor bilinear terms are used.…”

Section: B Literature Reviewmentioning

confidence: 99%

Transmission Expansion Planning Considering a High Share of Wind Power to Maximize Available Transfer Capability

et al. 2023

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To have an effective transmission network and support free trading, the open and nondiscriminatory access to transmission services for all market participants under all conditions is of significant concern. To this end, a sufficient value of available transfer capability (ATC) is required, which can significantly affect the electricity market efficiency. This paper constructs a methodology for reinforcing an existing transmission network considering wind power investment to enhance ATC. In this regard, a bi-level structure is adopted whose upper level is the joint transmission expansion planning (TEP) and wind power investment subject to the technical power grid limitations. After solving the upper level, a security-constrained economic dispatch (SC-ED) is formulated to acquire the optimal generation scheduling. Then, the lower level is designed to calculate the ATC. To solve this problem, the SC-ED is first replaced by its primal and dual feasibility constraints and strong duality equality. Then, these constraints are added to the upper level. The lower level is also replaced by its corresponding constraints. Consequently, a bilinear single-level optimization problem is extracted, which is further solved by employing Konno's cutting plane algorithm. The experimental results on the IEEE 24-bus RTS show the proposed method's effectiveness. It is indicated that the ATC can be improved by 27.2 % using the formulated model at the expense of more investment cost resulting in less load shedding and less wind 𝜆 A variable indicating the growth in load or generation. 𝑃 Electric demand in the ATC evaluation problem. 𝑃 , 𝑃 , 𝑃 Power output of thermal units in the main problem, ATC evaluation problem and SC-ED problem. 𝑃 , 𝑃 , 𝑃 Power output of a wind farm in the main problem, ATC evaluation problem and SC-ED problem. 𝑃 Capacity of a wind farm. 𝑢 Binary variable indicating the installation status of a transmission line. 𝛿 ,𝛿 , 𝛿 Voltage angle in the main problem, ATC evaluation problem and SC-ED problem. 𝛿 , Voltage angle at the reference bus.

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“…Ref. [20] presents a distributionally robust optimization-based planning scheme considering load and wind uncertainties from the data-driven ambiguity set. Besides generating units and transmission lines, DR and solar power plants are also modeled as investment options.…”

Section: Generation Schedulingmentioning

confidence: 99%

A Joint Optimization Model for Transmission Capacity and Wind Power Investment Considering System Security

et al. 2023

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Due to continuous growth in electric demand and increasing connection of renewable energy, the power systems are being operated with smaller stability margins. Therefore, a sufficient loading margin is essential to maintain the system secure and ensure voltage stability. To this end, this paper studies the joint optimization of transmission capacity and wind power investment problem, the unique feature of which is incorporating voltage stability margin (VSM) in the planning model. A bi-level model has been formulated whose upper level minimizes the total investment and operation cost minus the weighted VSM. The lower level evaluates the VSM given the optimal expansion plan from the upper level. In addition, the stochastic nature of wind power and load can impact voltage stability. Thus, uncertainties related to intermittent wind generation and demand must be modeled. We use an approximated linear representation to model the AC power system at both levels of the problem. The duality theory (primal-dual formulation) is utilized to transform bi-level programming into single-level mathematical programming. The validity of the constructed methodology is demonstrated on the IEEE 24-bus RTS, which indicates the efficacy and feasibility of the presented model. INDEX TERMSBi-level programming, Primal-dual formulation, Transmission and wind investment, Voltage stability. NOMENCLATURE A. Indices and sets: Ω Set of buses indexed by 𝑖, 𝑗. Ω Set of scenarios indexed by s. B. Parameters: 𝑔 , 𝑏 Conductance and susceptance of a transmission line. 𝐴 , 𝐵 , 𝐶 Constant parameters used to approximate a circle by a regular polygon. 𝑎 , 𝑏 , 𝑐 Production cost coefficients of a thermal unit. 𝐼 ,𝐼 Annualized investment cost of a wind farm and a transmission line. 𝑀 , 𝑀 , 𝑀 , 𝑀 Big-M parameters. 𝑃 Active power demand. 𝑄 , 𝑄 , 𝑄 Reactive power generation of thermal units in the main problem, VSM assessment problem and OPF problem. 𝑃 , 𝑃 , 𝑃 Active power generation of wind farms in the main problem, VSM assessment problem and OPF problem. 𝑄 , 𝑄 𝑄 Reactive power generation of wind farms in the main problem, VSM assessment problem and OPF problem. 𝑃 Capacity of a wind farm. 𝑢 Binary variable indicating the installation status of a transmission line. ∆𝑣 , ∆𝑣 , ∆𝑣 Voltage deviation in the main problem, VSM assessment problem and OPF problem. 𝛿 ,𝛿 𝛿 Voltage angle in the main problem, VSM assessment problem and OPF problem. 𝛿 , Voltage angle at the reference bus.

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Distributionally Robust Coordinated Expansion Planning for Generation, Transmission, and Demand Side Resources Considering the Benefits of Concentrating Solar Power Plants

Cited by 20 publications

References 33 publications

Enhancing Generation Expansion Planning With Integration of Variable Renewable Energy and Full-Year Hourly Multiple Load Levels Balance Constraints

Enhancing Generation Expansion Planning With Integration of Variable Renewable Energy and Full-Year Hourly Multiple Load Levels Balance Constraints

Transmission Expansion Planning Considering a High Share of Wind Power to Maximize Available Transfer Capability

A Joint Optimization Model for Transmission Capacity and Wind Power Investment Considering System Security

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