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This paper proposes a real-time joint regulating reserve deployment (RRD) model of electric vehicles (EVs) and coal-fired generators (CGs) considering EV battery degradation. The superiority of the proposed model lies in the integration of the EVs' fast regulation speed and CGs' large regulation capacity. The whole optimization model is formulated under a two-stage Markov Decision Process (MDP) to correspond to the real-time RRD process. The user behavior characteristic and EV battery degradation are taken into account to stimulate the users to participate in RRD. Based on the formulated MDP, a piece-wise linear (PWL) function based scalable approximate dynamic programming (ADP) algorithm for different EV clusters is constructed to solve the real-time RRD model under uncertainties. A separable slope update method is proposed to update the slopes of PLF for different EV clusters with different user behaviors. The proposed ADP based real-time RRD algorithm (ADP-RTRRD) provides the approximate optimal real-time RRD policy with the empirical knowledge embedded through off-line training. Numerical simulation on a modified IEEE-39 bus system and Henan power grid in China verify the superiority of the proposed joint RRD model and the advantages of the proposed ADP-RTRRD algorithm. • Proposing the joint RRD model of EVs and CGs considering EV battery degradation. • Taking advantage of EVs' fast regulation speed and CGs' large regulation capacity. • Applying EV aggregation methods to simplify optimization complexity. • Reformulating the real-time RRD process under a two-stage MDP. • Proposing a scalable ADP algorithm to obtain approximate optimal solution. [ABSTRACT FROM AUTHOR]

• Building the stochastic multi-time period optimization model for IHPS. • Proposing a new stochastic optimization for IHPS namely MADP. • Taking advantage of multi-energy integration in real-time schedule. • Coping with uncertainties with empirical knowledge embedded. • Validating the effectiveness of the MADP in real-time optimization. This paper proposes a multi-dimensional approximate dynamic programming (ADP) algorithm for the real-time schedule of integrated heat and power system (IHPS) with battery and heat storage tank (HST). The multi-time period optimization problem is reformulated under the Markov Decision Process. The high dimensional state variables are aggregated into the state of charge (SOC) of battery and overall available heat (OAH) of HST to reduce the computation of value function approximation (VFA) while ensuring approximate accuracy. Under sufficient training by uncertainty scenarios of wind power, electricity price, electrical and heat load, the approximate value function (AVF) can derive empirical knowledge and help IHPS make decisions to cope with uncertainties. The proposed ADP algorithm can efficiently take advantage of multi-energy integration and provide a near-optimal operation strategy to ensure the economy of IHPS by recursively solving the Bellman's equation. Simulation results compared with existing methods validate the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• Proposing a new robust co-planning of AC/DC hybrid transmission network and energy storage. • Embedding BFM within the proposed co-planning model. • Using data-adaptive robust optimization to cope with uncertainties. • Validating the effectiveness of the proposed method in practical power system. This paper proposes a robust co-planning model of hybrid AC/DC transmission network and energy storage with the penetration of renewable energy to promote the accommodation of renewable energy and to avoid investment redundancy. The energy storage configured in the power grid can improve the power flow distribution and alleviate transmission congestion, postponing the investment of new devices. A deterministic co-planning model is firstly developed considering voltage fluctuation, reactive power flow and the flexibility of voltage source converter based high voltage direct current (VSC-HVDC). To address the solving complexity caused by the non-convexity of the model, second-order cone programming (SOCP) is applied to transform the proposed model into a convex problem. To cope with the uncertainty of renewable energy, the robust co-planning formulation is established, where the data-adaptive uncertainty set with the extreme scenario method is introduced to describe renewable generation uncertainty. The column-and-constraint generation (C&CG) algorithm is adopted to decompose the robust co-planning problem into a master problem and several slave problems, which reduces the calculation scale and accelerates the solving process. Two case studies on a modified Garver's 6-bus system and a practical Jiangxi province power system in China are carried out to verify the effectiveness and superiority of the proposed robust co-planning model. [ABSTRACT FROM AUTHOR]

The day-ahead operational schedule of the integrated electricity and heat system may be suboptimal due to prediction errors of renewables and loads. This paper proposes a real-time optimal operation scheme for the integrated electricity and heat system considering reserve provision from large scale heat pumps, which utilizes model predictive control and different operating reserves to gradually balance forecast errors of renewables and loads in the co-optimization of reserve deployment and heat regulation. The real-time operation is divided into two stages including real-time pre-scheduling and real-time balancing. A two-stage model predictive control approach is proposed to deploy following reserve and regulating reserve for real-time pre-scheduling and real-time balancing, respectively. The following reserve in the real-time pre-scheduling is used to deal with the day-ahead forecast errors, while the regulating reserve in the real-time balancing is to handle real-time forecast errors. In addition, a detailed reserve provision model of large-scale heat pumps is built. The case studies are conducted on a 6-bus integrated electricity and heat system. The simulation results show that the proposed two-stage approach uses following and regulating reserves from large-scale heat pumps to further reduce operational cost, wind power curtailment, and load shedding. The MPC approach can obtain a feasible solution closer to the ideal solution. • Real-time optimal operation of IEHS with real-time prescheduling and balancing. • Large-scale heat pumps providing following and regulating reserves. • Reserve deployment and heat regulation are co-optimized. [ABSTRACT FROM AUTHOR]