Congestion problems might occur in distribution networks as the penetration of distributed energy resources (DERs) progresses. This study focuses on the complementarity of multiple energy resources in energy hubs (EHs) to solve possible distribution network congestions. First, we consider an EH in which combined cooling, heating and power (CCHP) units and heat pumps are integrated with renewable energy resources. The EH models the intrinsic coupling relationship among various energy carriers, forming a flexible operation where electricity, natural gas, cooling and heat can complement each other. Next, an optimal operation strategy of multiple energy hubs enables gas-to-electricity to provide local energy supply for EH during electricity peak periods, and consume renewable energy generation by the complementarity of electricity, heat and cooling. In addition, the uncertainty of renewable energy resources is taken into account via scenario-based stochastic programming. Numerical results show that the proposed operation strategy explores EHs' operation flexibility to mitigate distribution network congestion when we consider high renewable energy penetration in power systems.

Grid connection of intermittent renewable energy such as wind power and photovoltaic results in challenges of keeping power balance for power system operation. In order to solve this problem, this paper proposed a multi-time scale coordinated scheduling model for the combined system of Wind power-Photovoltaic-Thermal generator-Hydro pumped storage-Battery (WPTHB) by taking advantages of their complementary operation characteristics. The scheduling model is composed of three time scales: the day-ahead scheduling, the 1-hour ahead scheduling and 15-minute ahead scheduling. 1) in the day-ahead scheduling, based on the 24-hour ahead forecast data of Wind-Photovoltaic power and Load demand (WPL), the optimal power outputs of thermal power units are solved from a mix integer linear programming (MILP) model to achieve the minimal operation cost of thermal units. 2) In the 1-hour ahead scheduling, based on power output of thermal units optimized in the day-ahead scheduling and the hourly forecasted WPL, the hydro pumped unit power outputs are optimally dispatched to minimize their operation cost. 3) In the 15-minute ahead scheduling, based on day-ahead optimal power outputs of thermal units and the 1-hour ahead optimal outputs of pumped storage, the battery optimal power generation is obtained from an AC optimal power flow model solved by MATPOWER. Simulation of the New England system has validated that the proposed multi-time scale coordinated scheduling model could fully explore the distinguished power regulation speed and capacities of thermal power units, hydro pumped storages and batteries to effectively track WPL variations and achieve system economic operation simultaneously.

This paper proposes a distributed multi-energy management framework for the coordinated operation of interconnected biogas-solar-wind microgrids. In this framework, each microgrid not only schedules its local hybrid biogas-solar-wind renewables for coupled multi-carrier energy supplies based on the concept of energy hub, but also exchanges energy with interconnected microgrids and via the transactive market. The multi-microgrid scheduling is a challenging optimization problem due to its severe constraints and strong couplings. A multi-microgrid multi-energy coupling matrix is thus formulated to model and exploit the inherent biogas-solar-wind energy couplings among electricity, gas and heat flows. Furthermore, a distributed stochastic optimal scheduling scheme with minimum information exchange overhead is proposed to dynamically optimize energy conversion and storage devices in the multi-microgrid system. The proposed method has been fully tested and benchmarked on different scaled multi-microgrid system over a 24-hour scheduling horizon. Comparative results demonstrated that the proposed approach can reduce the system operating cost and enhance the system energy-efficiency, and also confirm its scalability in solving large-scale multi-microgrid problems.

This paper proposes a novel probabilistic transient stability constrained optimal power flow (P-TSCOPF) model to simultaneously consider uncertainties and transient stability for power system preventive control. While detailed wind generator model with rotor flux magnitude and angle control strategy is used to describe the dynamic behaviors of wind generators, uncertain factors with correlations, such as probabilistic load injections, stochastic fault clearing time, and multiple correlated wind generations, are also included to form a representative P-TSCOPF model. A new GSO-PE approach, consisting of an improved group search optimization (GSO) and 2m + 1 point estimated (PE) method with Cholesky decomposition, is then designed to effectively solve this challenging P-TSCOPF problem. The proposed P-TSCOPF model and GSO-PE solution approach have been thoroughly tested on a modified New England 39-bus system with correlated uncertain wind generations. Comparative results with Monte Carlo (MC) simulations have confirmed the validity of the P-TSCOPF model and demonstrated the effectiveness of GSO-PE method.

Internet data centers are booming in the last few years, leading to gigantic amount of energy consumption and greenhouse gas emission. In this paper, a data center microgrid including conventional units, energy storage, and renewable generation is modeled to optimize its electricity bill and carbon footprint. Considering the randomness of electricity price, renewable output and workload distribution, the volatility risk is incorporated to measure the associated operation risk. Then, a day-ahead emission-aware stochastic resource planning scheme is formulated to decide the strategy on power procurement, energy storage operation, batch workload allocation, and unit commitment of conventional units. Simulation results show the effectiveness of the proposed approach.