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The growing uncertainty caused by the increasing wind power penetration brings an urgent need of increased reserve capacity to ensure the secure operation of power systems. The interconnection of multiple integrated electricity and heating systems (IEHSs) allows resource sharing among them and the district heating system can provide additional reserves to the electric power system. In this paper, a decentralized robust energy and reserve co-optimization for multiple IEHSs is proposed to handle wind power uncertainty in a more economic and efficient way. In each IEHS, the day-ahead energy and reserve scheduling is formulated as a two-stage adaptive robust optimization, where the feasibility of day-ahead reserve deployment in the real-time operation is guaranteed. Then, a decentralized methodology is developed based on the alternating direction method of multipliers (ADMM) to achieve the synergistic yet independent operation by exchanging tie-line power flows with adjacent IEHSs. In order to accelerate the convergence of the decentralized operation and reduce the communication demand, the improved ADMM with locally adaptive penalty parameters is adopted. Simulation results demonstrate the effectiveness of the proposed decentralized robust energy and reserve optimization scheme in terms of improving the economic efficiency and ensuring the secure operation for IEHSs, as well as preserving information privacy. • A decentralized energy and reserve co-optimization for multiple IEHSs is proposed. • The feasibility of reserves is guaranteed with the two-stage robust optimization. • An improved ADMM algorithm with adaptive penalty parameters is adopted.. • The economic efficiency of IEHSs is improved and information privacy is preserved. [ABSTRACT FROM AUTHOR]

• A two-stage adaptive robust energy and reserve co-optimization scheme is proposed. • The spatial correlation of wind uncertainty is used to control the conservatism of robust solutions. • The available reserve capacity of combined heating and power units is fully utilized. • Electrical boiler and buildings' thermal inertia are introduced to increase the reserve capacity. The uncertainty and variability of wind power pose significant challenges to secure and reliable operation of power systems. Coordinated operation of the electric power system and district heating system, which can provide sufficient reserve capacity and flexibility, is an effective way to cope with the uncertainty. This paper proposes an adaptive robust energy and reserve co-optimization for the integrated electricity and heat system to minimize the total system cost under the worst-case realization of wind uncertainty considering spatial correlations of wind uncertainties. The available reserve capacity and flexibility provided by the district heating system is fully used by exploiting the regulation capabilities of combined heat and power units and electrical boilers, as well as utilizing the building thermal inertia. To reduce the conservatism of the robust solution, the spatial correlation of wind uncertainties is considered in the uncertainty set. The column-and-constraint generation algorithm is adopted to solve the adaptive robust model iteratively by reformulating the second stage with its Karush-Kuhn-Tucker conditions. Simulation results demonstrate that the economic efficiency is improved by utilizing the reserve flexibility from the district heating system and considering wind farm spatial correlations. Compared with the conventional single-stage optimized model, the feasibility of the two-stage robust solution is always guaranteed by considering the real-time operation constraints of the electric power system and district heating system. [ABSTRACT FROM AUTHOR]

Synchronous generators are operating for less time than before or being decommissioned in the National Electricity Market (NEM) of Australia, due to the proliferation of asynchronous wind and solar generation. Sub-networks of the NEM will face inertia shortages in the near future. This paper develops a formulation of zonal inertia constrained generator dispatch for power systems with a diversified generator portfolio including synchronous generators, synchronous condensers, inverter-interfaced generators and energy storages. Zonal inertia constraints are formulated in unit commitment and optimal power flow to limit the rate of change of frequency (RoCoF) in the event of network separation. Load frequency relief is also considered to reduce the ramp rate requirement of primary reserve. The proposed formulation can reduce the average cost of primary reserve and maintain zonal inertia adequacy to constrain RoCoF in case of the trip of the interconnector(s). [ABSTRACT FROM AUTHOR]

• Explore benefits of using large-scale heat pumps to provide following and regulating reserves. • Stochastic optimization of the integrated electricity and heat system co-optimizes energy, reserve, and heat regulation. • Identify inactive transmission line constraints using an analytical calculation algorithm. Integrating power and heat sectors can increase the flexibility of wind power integration. Power-to-heat devices powered by renewables can promote the electrification and decarbonization of the heat sector. This paper explores the feasibility and benefits of using large-scale heat pumps for reserve provision. A two-stage stochastic optimization model is proposed for the day-ahead centralized scheduling of the integrated electricity and heat system considering wind power uncertainties, with reserve provision of large-scale heat pumps, combined heat and power units, and thermal power units. The model co-optimizes energy, following reserve, regulating reserve, and heat regulation to handle wind power uncertainties. To reduce the computational complexity of the scenario-based stochastic optimization problem, inactive transmission line constraints are identified and removed based on an analytical calculation method, which considers the influence of power consumption of large-scale heat pumps on the power flow of transmission lines. The benefits of using reserves from large-scale heat pumps are tested on the modified IEEE-118 bus integrated electricity and heat system. The simulation results show that using large-scale heat pumps to provide the following reserve and regulating reserve show similar impacts on saving cost and reducing wind energy curtailment, and the acceleration algorithm can save computation time by approximately 64%. [ABSTRACT FROM AUTHOR]

• Wind power forecast uncertainty in integrated electricity and natural gas systems are modelled. • The spatial-temporal correlation of wind power plants power uncertainty is considered. • A distributionally-robust chance constrained model controls the dispatch robustness to the wind power forecast errors. • An interval optimization model considers the uncertainty in natural gas network. With increasing penetrations of gas-fired generation in power systems because of reducing gas prices and emissions regulations, the interdependent and coordinated operation of integrated electricity and natural gas systems (IEGSs) is becoming an urgent research topic. Meanwhile, the significantly increasing deployment of wind power necessitates that IEGSs operation considers wind power output uncertainty. How to model the impact of wind power uncertainty on IEGSs power and gas flows dispatch is challenging. In this paper, a hybrid distributionally-robust chance-constrained and interval optimization (DRCC-IO) based model is proposed to consider the influence of wind power uncertainty and its spatial-temporal correlation on IEGSs operation. First, the DRCC-OPF model is proposed to obtain reliable economic dispatch solutions for the electricity network considering the wind power forecast errors. The spatial-temporal correlation of the wind power plant (WPP) forecasts is considered with a sparse correlation covariance matrix. Then, the interval optimization (IO) model is used to model the impacts of the power variations of gas-fired units on the natural gas network. Finally, the proposed model considers the impacts of wind power uncertainty on both the electricity and natural gas networks. Case studies performed on a six-bus power system coupled with a seven-node gas system and an IEEE 118-bus power system with a 14-node gas system verify the effectiveness of the proposed method to improve system security and reduce costs of the IEGSs. The robustness of the wind power forecast errors can be controlled in the proposed model to trade off the security and costs of the IEGSs. [ABSTRACT FROM AUTHOR]