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Affected by extreme hot and dry weather events, the power supplied by hydropower is seriously insufficient in Sichuan province during the summer of 2022, causing severe power shortage and incalculable economic losses for the society. Therefore, a novel multi-objective scheduling model based on grid-connected hydro-wind-solar-battery energy resources combining flexible reserves is proposed to collaboratively guarantee the power supply reliability of both the contracted long-distance power transmission and the local electricity demand under extreme hot and dry weather. The intermittent nature of water inflow, wind speed, solar irradiance, and load is considered, and the multi-scenarios are produced using Latin Hypercube Sampling method. In this work, thermal plants are regarded as flexible reserves for easing the load shedding level of local load. The McCormick's Envelope method is implemented to linearize the power production model of the hydropower station. A multi-objective model, including minimum penalty cost of load shedding and carbon dioxide emission, is proposed to achieve the economic-environmental equilibrium of the power system. The proposed multi-objective optimal hybrid energy complementary strategy is transformed into a single objective model via ε -constraint method, and the fuzzy satisfying method is introduced to select the best compromise solution in the Pareto Front. The meteorological information of the summer of 2022 is collected to verify the effectiveness of this proposed mixed-integer-linear-programming energy complementary model based on a hydropower station on the Jinsha River in Sichuan province, China. Numerical experiments demonstrate that the proposed model is efficient and can enhance the reliability of real-world power systems under extreme hot and dry weather. A set of policy suggestions have been provided for Sichuan province to develop the power grid to face the extreme weather effect. [ABSTRACT FROM AUTHOR]

Achieving the goal of sustainable development is dependent on the widespread integration of renewable energy sources, energy storage systems (ESSs), and electric vehicles (EVs). However, a continuous increase in the penetration of such elements would bring more complexities to the distribution network. Accordingly, this paper presents a unified planning model comprising renewable energy-based distributed generation (DG), ESS, and electric vehicle charging stations (EVCSs). In this regard, a Latin Hypercube Sampling method is utilized to generate multi-scenario for describing the uncertainty of renewable energy and load demand. The stochastic EV charging behaviors are represented by various probability density functions (PDF). In addition, an exploitable capacity loss of ESS and EV batteries is calculated by the battery degradation model based on the depth of discharge (DOD). Furthermore, the battery degradation cost is incorporated into the objective of the planning model to identify the optimal decision for candidate assets. A piecewise linearization approach is introduced to convert the problem into a mix-integer linear programming (MILP) model. Numerical results demonstrate that the exploitable capacity loss of batteries plays a key role in asset planning and provides potential contributions to the optimal decisions of the distribution network. In the meantime, by considering battery degradation in the optimization model, the sustainability and lifetime of the battery can be preserved. • Uncertainties include the renewable energy, load demand, and behaviors of electric vehicles are considered. • Renewable energy-based DG, ESS, and EVCS are involved in the unified planning model. • The degradation model is proposed to analyze the exploitable capacity loss of ESS and EV battery. • The enhancement of investment decision while maintaining the lifetime of battery is achieved. [ABSTRACT FROM AUTHOR]

Reliability improvement is regarded as a crucial task in modern distribution network expansion planning. Compared to previous works, this paper presents a bi-level optimization model to optimize the planning of the distribution network complying with multiple renewable energy and energy storage system (ESS) functionalities to guarantee the economical and reliable operation of the distribution network. The candidate assets include substations, distribution lines, renewable energy-based distributed generations (DGs), and ESSs are systematically involved. The load level affected by seasonal change and the multiple uncertainties, including renewable energy, load fluctuation, and contingency outage, are comprehensively considered. The uncertainties caused by the stochastic of renewable energy and load demand are described using Latin Hypercube Sampling (LHS) method. To address the computational burden and complexities associated with non-linear AC power flow, the mixed-integer linear programming (MILP)-based bi-level model is proposed via piecewise linearization methodology. Therein, the upper-level optimization model is proposed to minimize the total present value cost of the planning scheme in normal operating conditions. The lower level model, which is constrained to investment decision-making of the upper-level framework, aims to minimize the total cost of expected energy not supplied (EENS) considering the uncertainties of the single contingency outage. The effectiveness of the proposed bi-level planning model is validated by numerical studies to guarantee economic and reliability improvement for distribution network. • Multiple uncertainties are comprehensively considered in the proposed model. • Branches and multi-type generators are incorporated to the joint planning model. • Optimal allocation and reliability assessment are coordinated to build bi-level model. • The benefits from ESS for reliability enhancement is analyzed. [ABSTRACT FROM AUTHOR]