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This paper proposes a bi-probability-interval optimization (BPIO) model for wind-solar power day-ahead scheduling (WSP-DAS) under uncertainties, which aims to obtain the optimal trade-off planning by balancing profit and risk brought by uncertain wind power and solar power penetration. First, the confidence intervals of wind power and solar power are formulated under given confidence levels of wind speed and solar radiation, respectively. Then, the distribution probabilities of each wind power and solar power are obtained accordingly based on the cumulative distribution function (CDF) of wind speed and solar radiation, respectively. Finally, the framework of BPIO based WSP-DAS is developed to balance the profit and risk, considering a conditional expectation based optimization objective. Comparative experiments are conducted on two day-ahead scheduling systems under the dynamic uncertain wind and solar power penetration. The empirical results fully demonstrate that the proposed BPIO can significantly improve the reliability and effectiveness of evaluating WSP-DAS, in terms of obtaining a trade-off planning between profit and risk against the integration of uncertain wind-solar power. [ABSTRACT FROM AUTHOR]

Highlights • A risk aversion active distribution network (ADN) model is developed. • The uncertainties of electricity price and wind power are considered in AND planning. • The proposed model can result in a trade-off planning between the profit and risk. • The reliability and effectiveness of the proposed model are discussed. Abstract This paper introduces a new computational framework to account for uncertainties in active distribution network (ADN) planning in the presence of wind power, energy storage and electricity price. A key challenge when dealing with wind power and electricity price is the inaccuracy of forecasting methods because of the uncertainty of distributed power such as wind power, and the different demand response providers. We propose a risk aversion planning model for ADN with respect to uncertainties of wind power and electricity price. The proposed model needs neither sampling different scenario nor to calculate multi-objective while guaranteeing a quantifiable risk level by a conditional expectation. Then, an electricity price driven ADN planning has been discussed, and the problem of selecting custom electricity contracts and finding the optimal procurement strategy of meeting contract obligations under spot price uncertainty is considered. Finally, simulation studies are carried out for several IEEE test systems to validate the effectiveness of the proposed model in obtaining the optimal trade-off solution between profit and risk. [ABSTRACT FROM AUTHOR]

Increasing deployment of wind power into integrated energy system becomes a critical pathway for deep decarbonizing the power sector. It is imperative to investigate the impacts of uncertain wind power on integrated power system as the result of the heavy interdependencies among different types of energy in supplying the requirements of economy, low carbon and risk aversion. Here, the concept of fuzzy semi-entropy is presented to quantify the downside risk of uncertain wind power. After that, a mean-semi-entropy model for low-carbon oriented electrical-gas-heat energy system including carbon emission, carbon capture and carbon trading is proposed. In particular, the dependence of wind power outputs from different wind turbines is investigated by the multivariate Gaussian Copula. Results obtained from case studies demonstrate the feasibility of the proposed model in achieving a balanced trade-off among system economy, low carbon emission and operation risk properly. The proposed model enables the reduction in total cost and carbon emission by 2.29% and 5.18% in comparison to these of traditional cost minimization model. In addition, the accommodation level of wind power with multivariate Gaussian Copula improves by 2.17% compared to that without considering coupling. • Low-carbon technologies considering emission deviations are studied. • A credibility theory-based semi-entropy is presented for downside risk. • The multivariate Gaussian Copula is developed to study the dependence. • The model can be easily extended to other energy system optimizations. [ABSTRACT FROM AUTHOR]

Due to the uncertainty of wind power and complex constraints of available hydro, short term hydro-wind-thermal scheduling (HWTS) is one of the most difficult optimization problems in the operational planning of power systems. This paper presents a risk-aware optimization model, named probability interval optimization (PIO), to reliably evaluate the HWTS from the perspective of risk and profit. In PIO, the uncertain wind power is deemed as a probability interval variable, the risk of wind power is assessed by its probability distribution, and the profit is manifested by the decrease of generation cost between the same system with and without wind power integrated. For solving the PIO model, an evolutionary predator and prey strategy (EPPS) is proposed in this paper. The EPPS focuses on dynamically adjusting the algorithm’s exploration and exploitation abilities by introducing an escaping mechanism and a classification mechanism. In addition, a heuristic repair mechanism, instead of penalty function approach, is applied to handle the complex equality and inequality constraints of HWTS. Simulation studies based on three HWTS systems demonstrate that the risk-aware PIO model is well reliable and applicable to solve HWTS considering the uncertain wind power integrated, the EPPS algorithm can obtain superior solutions in comparison with other recently developed algorithms, and the heuristic repair mechanism is efficient for dealing with complex constraints of HWTS. [ABSTRACT FROM AUTHOR]

Agricultural microgrid provides a promising solution for energy supply of rural areas in a cost-effective way. In this paper, the principle of wind-pumped storage integrated agricultural microgrid to fulfill both the electric and water load demand is explored. From the perspective of risk aversion, the indexes of expected power not served (EPNS) and expected power curtailment (EPC) are derived to evaluate the uncertain wind power. Then, a stochastic day-ahead scheduling model of wind-pumped storage power system and irrigation system is proposed to minimize the total operation cost whilst achieve the peak load shaving by taking the advantage of wind-pumped storage compensation. The proposed model is a complex optimization problem that takes into account distributed generations, electrical and water demand, turbine flow rate, and irrigation time and volume. We propose a intraspecific competition (IC) based evolutionary predator and prey strategy (EPPS) to solve the model. Finally, simulation results conducted on an agricultural microgrid have verified that, compared to previous works in the literature, the proposed model can effectively improve the utilization of wind power, reduce the operation cost of microgrid as well as smooth load curve. Additionally, the proposed algorithm can get a more competitive solution than other recently developed algorithms. • A stochastic agricultural microgrid is established with power and irrigation systems. • The indexes of EPNS and EPC are derived from the perspective of risk aversion. • An IC based EPPS is proposed to solve complexity optimization problems. • Comprehensive studies have been conducted on the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

Multi-energy supply system is under development with a variety of benefits for microgrid operation. In this paper, the coordinated scheduling and optimal operation strategy of coupled heat-power-gas (CHPG) microgrid are studied for flexibility improvement in consideration of implementing cogeneration technology and power to gas (P2G) technology. To enhance the operational flexibility of the CHPG in supplying multiple energy demands, an integrated demand response (IDR) model including power and gas demand response is developed by converting multiple energy sources into one another and changing the energy consumption pattern of customer under a given period. Whilst, the customer satisfaction is also taken into account in IDR model. Moreover, a credibility theory-based risk measure is presented for quantitatively assessing the randomness and fuzziness characteristics of uncertain wind power, and then we explore the balance between the operation cost and risk of microgrid with wind power integration. Case studies are undertaken on the CHPG microgrids considering IDR and uncertainty. Simulation results indicate that the proposed coordinated scheduling model and the optimal operation method are universal and effective over the entire multi-energy dispatching horizon. • A CHPG microgrid is established in consideration of P2G and cogeneration technology. • The IDR is presented to improve the flexibility of multi-type energy demand and shed peak load. • The credibility measure is developed to fully quantify the potential risk of uncertain wind power. • The coordinated scheduling strategy improves wind power accommodation, reduces operation cost and flattens load curve. • The impact of customer satisfaction and dissatisfaction models is considered in CHPG microgrid. [ABSTRACT FROM AUTHOR]

• A cost-effective two-stage optimization model is proposed for microgrid planning and scheduling. • The compressed air energy storage is used to deal with wind power randomness. • Preventive maintenance is used to prevent distributed generations random failure. • A risk aversion model is derived to against uncertain wind power. This paper proposes a cost-effective two-stage optimization model for microgrid (MG) planning and scheduling with compressed air energy storage (CAES) and preventive maintenance (PM). In the first stage, we develop a two-objective planning model, which consists of power loss and voltage deviation, to determine the optimal location and size of MG. Then, a stochastic scheduling model is presented in the second stage to balance outputs of distributed generations (DGs), charging and discharging power of CAES, power exchange costs of MG and PM costs of DGs. Whilst we derive a credibility assessment-based risk aversion model, named conditional value-at-credibility (CVaC), to hedge against uncertain wind power. The proposed model has been evaluated on the IEEE testing system and numerical results demonstrate the effectiveness of the model by providing the optimal trade-off solution in terms of the economy and security. [ABSTRACT FROM AUTHOR]

• Both probability and credibility measures are studied for assessing wind power. • Conditional value-at-credibility is presented to reach a trade-off solution. • A peak-flat-valley partition model is investigated for wind farm integrated system. • The impacts of the proposed method are fully analyzed. As wind energy increasingly penetrates into power systems, new challenges arise for the dispatcher to keep the systems reliable under uncertain circumstances. To solve this problem, a conditional value-at-credibility (CVaC) model is proposed in this paper for hedging random fuzzy wind power in demand response integrated multi-period economic emission dispatch (MEED). In the model, a Gaussian distribution-based probability measure is presented to assess wind randomness with wind farm wake effects. Whilst a Cauchy distribution-based credibility measure is derived to assess wind fuzziness. After that, the wind power is deemed as a random fuzzy variable, and the objective function with respect to CVaC is developed to balance the risk and the profit of MEED with the wind farm's integration. In addition, this paper presents an incentive-based demand response that incorporates a peak-flat-valley period partition mechanism into the time of use strategy to find the optimal incentive in shedding peak load. A novel global optimization algorithm is then established to solve the proposed optimization model. Case studies prove the feasibility and effectiveness of the proposed model in solving MEED with uncertain wind power by providing the optimal trade-off solution between economy and security. [ABSTRACT FROM AUTHOR]

Standalone renewable energy system holds the most promising solution to the electrification of remote areas without utility grid access as well as to reduce fossil fuel consumption and environmental pollution. However, the random volatility and unpredictability of renewable energy are key factors to restrict its large-scale accommodation. In the present study, a multi-objective mean-semi-entropy model is proposed for a standalone micro-grid with photovoltaic-wind-battery-diesel generator hybrid system, with the aim of providing a trade-off solution between maximum profits and minimum risk in consideration of photovoltaic and wind uncertainties. Then, the preference-inspired co-evolutionary algorithm, along with Pareto optimality concept, is used for the system techno-economic optimization, i.e., to maximize the profits defined as the mean value of the return and to minimize the risk defined as the semi-entropy simultaneously. Subsequently, the preference ranking organization method is used for decision making to determine the optimal trade-off dispatch solution. Simulation results show that the multi-objective mean-semi-entropy model is well applicable to deal with standalone micro-grid operation, considering the integration of uncertain renewable energy resources. • A planning method for standalone micro-grid with uncertain wind and photovoltaic power is investigated. • A multi-objective mean-semi-entropy model for optimal standalone micro-grid operation is proposed. • An optimal dispatch solution from the perspective of risk aversion and profit maximization is studied. • Different models are employed for comparison to fully evaluate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]