Real-time scheduling strategy for microgrids considering operation interval division of DGs and batteries

Real-time scheduling as an on-line optimization process must output dispatch results in real time. However, the calculation time required and the economy have a trade-off relationship. In response to a real-time scheduling problem, this paper proposes a real-time scheduling strategy considering the operation interval division of distributed generators(DGs) and batteries in the microgrid. Rolling scheduling models, including day-ahead scheduling and hours-ahead scheduling, are established, where the latter considers the future state-of-charge deviations. For the real-time scheduling, the output powers of the DGs are divided into two intervals based on the ability to track the day-ahead and hours-ahead schedules. The day-ahead and hours-ahead scheduling ensure the economy, whereas the real-time scheduling overcomes the timeconsumption problem. Finally, a grid-connected microgrid example is studied, and the simulation results demonstrate the effectiveness of the proposed strategy in terms of economic and real-time requirements.

Cyber-secure decentralized energy management for IoT-enabled active distribution networks

This paper provides a strategic solution for enhancing the cybersecurity of power distribution system operations when information and operation technologies converge in active distribution network(ADN). The paper first investigates the significance of Internet of Things(IoT) in enabling fine-grained observability and controllability of ADN in networked microgrids. Given severe cybersecurity vulnerabilities embedded in conventionally centralized energy management schemes, the paper then proposes a cyber-secure decentralized energy management framework that applies a distributed decision-making intelligence to networked microgrids while securing their individual mandates for optimal operation. In particular,the proposed framework takes advantage of software-defined networking technologies that can secure communications among IoT devices in individual microgrids, and exploits potentials for introducing blockchain technologies that can preserve the integrity of communications among networked microgrids in ADN. Furthermore, the paperpresents the details of application scenarios where the proposed framework is employed to secure peer-to-peer transactive energy management based on a set of interoperable blockchains. It is finally concluded that the proposed framework can play a significant role in enhancing the efficiency, reliability, resilience, and sustainability of electricity services in ADN.

Multi-time Scale Optimal Power Flow Strategy for Medium-voltage DC Power Grid Considering Different Operation Modes

Direct current(DC)power grids based on flexible high-voltage DC technology have become a common solution of facilitating the large-scale integration of distributed energy resources(DERs)and the construction of advanced urban power grids.In this study,a typical topology analysis is performed for an advanced urban medium-voltage DC(MVDC)distribution network with DERs,including wind,photovoltaic,and electrical energy storage elements.Then,a multi-time scale optimal power flow(OPF)strategy is proposed for the MVDC network in different operation modes,including utility grid-connected and off-grid operation modes.In the utility grid-connected operation mode,the day-ahead optimization objective minimizes both the DER power curtailment and the network power loss.In addition,in the off-grid operation mode,the day-ahead optimization objective prioritizes the satisfaction of loads,and the DER power curtailment and the network power loss are minimized.A dynamic weighting method is employed to transform the multi-objective optimization problem into a quadratically constrained quadratic programming(QCQP)problem,which is solvable via standard methods.During intraday scheduling,the optimization objective gives priority to ensure minimum deviation between the actual and predicted values of the state of charge of the battery,and then seeks to minimize the DER power curtailment and the network power loss.Model predictive control(MPC)is used to correct deviations according to the results of ultra short-term load forecasting.Furthermore,an improved particle swarm optimization(PSO)algorithm is applied for global intraday optimization,which effectively increases the convergence rate to obtain solutions.MATLAB simulation results indicate that the proposed optimization strategy is effective and efficient.

Peer-to-Peer Trading in Distribution Systems with the Utility's Operation

To realize a liberalized peer-to-peer (P2P) electricity market in distribution systems with network security, this paper develops a general framework for P2P trading in distribution systems with the utility's operation. The model is formulated as a bi-level programming. The utility's operation is an upper level problem, where a calculation method of network usage charges for P2P trading is also proposed. Peers' P2P trading is a lower level problem. An iterative algorithm based on analytical target cascading (ATC) is proposed to solve the model, where the interactions between utility and peers are presented. Numerical results on the IEEE 33-bus system demonstrate that the proposed method realizes a liberalized P2P market and ensures network security in distribution systems.

Integration of power-to-hydrogen in day-ahead security-constrained unit commitment with high wind penetration

The increasing integration of variable wind generation has aggravated the imbalance between electricity supply and demand. Power-to-hydrogen(P2H) is a promising solution to balance supply and demand in a variable power grid, in which excess wind power is converted into hydrogen via electrolysis and stored for later use. In this study, an energy hub(EH) with both a P2H facility(electrolyzer) and a gas-to-power(G2P) facility(hydrogen gas turbine) is proposed to accommodate a high penetration of wind power. The EH is modeled and integrated into a security-constrained unit commitment(SCUC) problem, and this optimization problem is solved by a mixed-integer linear programming(MILP) method with the Benders decomposition technique. Case studies are presented to validate the proposed model and elaborate on the technological potential of integrating P2H into a power system with a high level of wind penetration(HWP).

Optimal sizing of PV and battery-based energy storage in an offgrid nanogrid supplying batteries to a battery swapping station

Nanogrids are expected to play a significant role in managing the ever-increasing distributed renewable energy sources. If an off-grid nanogrid can supply fullycharged batteries to a battery swapping station(BSS)serving regional electric vehicles(EVs), it will help establish a structure for implementing renewable-energyto-vehicle systems. A capacity planning problem is formulated to determine the optimal sizing of photovoltaic(PV) generation and battery-based energy storage system(BESS) in such a nanogrid. The problem is formulated based on the mixed-integer linear programming(MILP)and then solved by a robust optimization approach. Flexible uncertainty sets are employed to adjust the conservativeness of the robust optimization, and Monte Carlo simulations are carried out to compare the performance of the solutions. Case studies demonstrate the merits of the proposed applications and verify the approach.