Energy Management and Capacity Optimization of Photovoltaic, Energy Storage System, Flexible Building Power System Considering Combined Benefit

Building structures themselves are one of the key areas of urban energy consumption,therefore,are a major source of greenhouse gas emissions.With this understood,the carbon trading market is gradually expanding to the building sector to control greenhouse gas emissions.Hence,to balance the interests of the environment and the building users,this paper proposes an optimal operation scheme for the photovoltaic,energy storage system,and flexible building power system(PEFB),considering the combined benefit of building.Based on the model of conventional photovoltaic(PV)and energy storage system(ESS),the mathematical optimization model of the system is proposed by taking the combined benefit of the building to the economy,society,and environment as the optimization objective,taking the near-zero energy consumption and carbon emission limitation of the building as the main constraints.The optimized operation strategy in this paper can give optimal results by making a trade-off between the users’costs and the combined benefits of the building.The efficiency and effectiveness of the proposed methods are verified by simulated experiments.

Interaction Quantification of MTDC Systems Connected with Weak AC Grids

The small-signal stability of multi-terminal high voltage direct current(HVDC)systems has become one of the vital issues in modern power systems.Interactions among voltage source converters(VSCs)have a significant impact on the stability of the system.This paper proposes an interaction quantification method based on the self-/en-stabilizing coefficients of the general N-terminal HVDC system with a weak AC network connection.First,we derive the explicit formulae of self-/en-stabilizing coefficients for any N-terminal HVDC system,which can quantify the interactions through different paths analytically.The relation between the self-/en-stabilizing coefficients and the poles of the system can be used to evaluate the impact of the interactions on the system stability effectively.Then,we employ the obtained formulae to analyze the parameter sensitivity and explain how a parameter affects the stability of the system through different paths of interactions.Finally,extensive examples are given to demonstrate the effectiveness of the proposed method.

PV-based virtual synchronous generator with variable inertia to enhance power system transient stability utilizing the energy storage system

The Photovoltaic(PV)plants are significantly different from the conventional synchronous generators in terms of physical and electrical characteristics,as it connects to the power grid through the voltage-source converters.High penetration PV in power system will bring several critical challenges to the safe operation of power grid including transient stability.To address this problem,the paper proposes a control strategy to help the PVs work like a synchronous generator with variable inertia by energy storage system(ESS).First,the overall control strategy of the PV-based virtual synchronous generator(PV-VSG)is illustrated.Then the control strategies for the variable inertia of the PV-VSG are designed to attenuate the transient energy of the power system after the fault.Simulation results of a simple power system show that the PV-VSG could utilize the energy preserved in the ESS to balance the transient energy variation of power grid after fault and improve the transient stability of the power system.

Transmission Network Expansion Planning Considering the Generators’Contribution to Uncertainty Accommodation

This paper presents an optimization for transmission network expansion planning(TNEP)under uncertainty circumstances.This TNEP model introduces the approach of parameter sets to describe the range that all possible realizations of uncertainties in load and renewable generation can reach.While optimizing the TNEP solution,the output of each generator is modeled as an uncertain variable to linearly respond to changes caused by uncertainties,which is constrained by the extent to which uncertain parameters may change the operational range of generators,and network topology.This paper demonstrates that the robust optimization approach is effective to make the problem with uncertainties tractable by converting it into a deterministic optimization,and with the genetic algorithm,the optimal TNEP solution is derived iteratively.Compared with other robust TNEP results tested on IEEE 24-bus systems,the proposed method produces a least-cost expansion plan without losing robustness.In addition,the contribution that each generator can make to accommodate with every uncertainty is optimally quantified.Effects imposed by different uncertainty levels are analyzed to provide a compromise of the conservativeness of TNEP solutions.