This paper proposes a data-driven distributionally robust chance constrained real-time dispatch (DRCC-RTD) considering renewable generation forecasting errors. The proposed DRCC-RTD model minimizes the expected quadratic cost function and guarantees that the two-sided chance constraints are satisfied for any distribution in the ambiguity set. The Wasserstein-distance-based ambiguity set, which is a family of distributions centered at an empirical distribution, is employed to hedge against data perturbations. By applying the reformulation linearization technique (RLT) to relax the quadratic constraints of the worst-case costs and constructing linear reformulations of the DRCCs, the proposed DRCC-RTD model is cast into a deterministic linear programming (LP) problem, which can be solved efficiently by off-the-shelf solvers. Case studies are carried out on a 6-bus system and the IEEE 118-bus system to validate the effectiveness and efficiency of the proposed approach.

While reducing the carbon emissions of traditional coal-fired units, carbon capture and storage (CCS) technology can also provide sufficient carbon raw materials for power to gas (P2G) equipment, which helps to achieve the low-carbon dispatch of an integrated energy system (IES). In this paper, an extended carbon emission flow (ECEF) model integrating CCS-P2G coordinated operation and low-carbon characteristics of an energy storage system (ESS) is proposed. On the energy supply side, the coupling relationship between CCS and P2G is established to realize the low-carbon economic operation of P2G. On the energy storage side, the concept of “state of carbon (SOCB)” is introduced to describe the carbon emission characteristics of ESS to exploit the potential of coordinated low-carbon dispatch on both sides of energy release and energy storage. On the load side, a new carbon emission reduction mechanism that guides users to actively respond and reduce carbon emissions is proposed, namely a low-carbon demand response mechanism. This mechanism uses the node carbon intensity as guiding signals, and the willingness of carbon emission reduction or the price in the carbon market as incentive signals. Furthermore, a low-carbon economic dispatch model considering multiple uncertainties such as wind power output, electricity price, and load demands is established. In order to solve the model efficiently, the parallel multi-dimensional approximate dynamic programming (PMADP) algorithm is adopted, and the solution efficiency is significantly improved compared with stochastic optimization, without losing solution accuracy under a multi-layer parallel loop nesting framework. The effectiveness of the proposed model and method is verified on an E14-H6-G6 system and an E57-H12-G12 system.

The interest conflict among entities in the integrated energy system (IES) has a great challenge to operation decisions of IES. With regards to this, an optimal dispatching model of electric‐heat‐hydrogen IES based on Stackelberg game is proposed. First, an energy producer (EP) model is formulated which considered the full utilization of hydrogen energy and involved the conversion of hydrogen energy to electricity and heat energy. Meanwhile, the demand response amount is integrated into the objective function of load aggregator (LA) in order to encourage consumers to adjust their consumption behaviour. Second, by analyzing the characteristics of price information interaction among EP, energy system operator (ESO), and LA, the payoffs of each entity in IES are reformulated. Finally, a Stackelberg game model is established with ESO as the dominator guiding price information, EP and LA as the followers whose private information is confidential. Genetic algorithm and quadratic programming algorithm are employed to solve the developed model. Numerical experiments are carried out on an actual park‐level IES in northern China to demonstrate the effectiveness of the proposed model in promoting the benefit equilibrium among various entities.

The increasing high penetration of wind power will further increase the uncertainty in power systems, and three key issues should be addressed: 1) determining the maximum accommodation level of wind power without sacrificing system reliability; 2) quantifying the potential risk when the wind generation realization is beyond the prescribed uncertainty sets; and 3) how to reduce the risk loss. Motivated by these, a risk-based two-stage robust unit commitment (RUC) model is proposed to analyze the admissibility of wind power. In this model, the electricity storage system (ESS) is utilized for managing the wind power uncertainty to reduce the risk loss. Different from a determined uncertainty set in the previous RUC, the proposed method can flexibly adjust the uncertainty set by optimizing the operational risks including the wind spillage risk and load shedding risk. Conditional Value-at Risk (CVaR) is adopted to describe the risk loss when the real wind power output is beyond the predefined uncertainty set. Meanwhile, the low-probability, high-influence events are taken into the account based on CVaR to determine the optimal acceptable wind generation considering the tradeoff between reliability and economics. The proposed model is solved effectively by the modified column and constraint generation method. Case studies on two benchmark systems illustrate that the ESS can reduce the risk loss of power system and improve the ability to accommodate the uncertainty of wind generation.

With the integration of huge renewable energy sources (RESs) into active distribution networks, how to address the uncertainty outputs of RESs for the day-ahead volt/var control (VVC) is a significant challenge. This paper presents a chance constrained mixed integer second order cone model to handle the nodal power uncertainties and nonlinear branch flow equations. A direct and fast scenario generation method is proposed by employing the group division method and the seven-step probability distribution model of RESs outputs. An efficient and accurate solution method which only uses few larger probability level scenarios instead of all reserved scenarios is also proposed. Numerical simulations on the IEEE 69 standard system show the superiority of the proposed algorithm over traditional Monte Carlo sampling (MCS)-based methods.