• Energy Research
• other engineering and technologies close

This paper presents second-order cone programming (SOCP) and semidefinite programming (SDP) models to solve the multiperiod optimal control problem of unbalanced three-phase distribution grids with battery energy storage systems. The decision variables are the active and reactive power of the battery energy storage system. The objective is to minimize the power loss and energy purchase cost from the distribution substation. The optimal dispatch problem requires solution by volt/VAR optimization. The SOCP and SDP models ensure global optimum of original nonconvex nonlinear programming problem. Multiobjective volt/VAR optimization is performed and the benefits of reactive power support from battery energy storage systems are explored. A simulation-based heuristic to extract rank-one solution from the higher rank solution of SDP model is also proposed. Empirical results show the numerical stability and exactness of the proposed three-phase SOCP and SDP models.

This research investigates third harmonic injection applied to a modular multilevel converter (MMC) to generate a higher DC voltage. This is achieved using a proposed novel control scheme that activates existing submodules (SMs) in the converter arms. The technique is fundamentally different to, and does the reverse of, the well-known third harmonic injection techniques utilized to increase the AC output voltage in three-phase converter systems for a given DC link voltage. In the proposed scheme, the number of inserted SMs in each converter leg is greater than the number of SMs per arm, whence the MMC can operate with a higher DC link voltage while the SM number per arm and their capacitor voltages remain unchanged. This lowers the DC current and the DC transmission loss is significantly reduced by 22%. Station conduction losses with the operational scheme are lowered by 2.4%. The semiconductor current stresses are also lowered due to the reduced DC component of arm currents. Additionally, the phase energy variation is reduced by 18%, which benefits circulating current control. The operating principles are presented in detail and mathematical models for conduction losses, energy variation, and circulating voltage are derived. Simulation of a point-to-point HVDC system demonstrates the effectiveness of the proposed MMC operational scheme.

This paper proposes a multi-timescale volt/var optimization for the optimal dispatch of battery energy storage system in smart distribution grids. It aims to coordinate the substation on-load tap changer operation on slow-timescale (hourly basis) with the photovoltaic inverters and battery storage operations on fast-timescale (15 min basis). This coordination is achieved by using two-stage stochastic programming and implemented via model predictive control. The power loss and energy purchase cost are reduced while maintaining voltages within limits. The forecasting uncertainties are modeled by generating a large number of random scenarios and then subsequently reducing scenario numbers to establish a tradeoff between computational burden and accuracy of the solution. The mixed-integer second-order cone program (MISOCP) is formulated with reduced scenarios, which achieves global optimum. Simulation results demonstrate the effectiveness of proposed MISOCP model in keeping the voltages within limits under forecasting uncertainties.

Abstract Photovoltaic (PV) systems can exhibit rapid variances in their power output due to irradiance changes which can destabilise an electricity grid. This paper presents a quantitative comparison of the suitability of different electrochemical energy storage system (ESS) technologies to provide ramp-rate control of power in PV systems. Our investigations show that, for PV systems ranging from residential rooftop systems to megawatt power systems, lithium-ion batteries with high energy densities (up to 600 Wh L−1) require the smallest power-normalised volumes to achieve the ramp rate limit of 10% min−1 with 100% compliance. As the system size increases, the ESS power-normalised volume requirements are significantly reduced due to aggregated power smoothing, with high power lithium-ion batteries becoming increasingly more favourable with increased PV system size. The possibility of module-level ramp-rate control is also introduced, and results show that achievement of a ramp rate of 10% min−1 with 100% compliance with typical junction box sizes will require ESS energy and power densities of 400 Wh L−1 and 2300 W L−1, respectively. While module-level ramp-rate control can reduce the impact of solar intermittence, the requirement is challenging, especially given the need for low cost and long cycle life.

Energy storage systems are widely used for compensation of intermittent renewable energy sources and restoration of system frequency and voltage. In a conventional operation, all distributed energy storage systems are clustered into one fixed virtual power plant and their state of charges are maintained at a common value. In this article, it is proposed to dynamically cluster the energy storage systems into several virtual power plants based on the energy storage systems’ power demands and capacities. This results in reduced network power losses. The proposed dynamic clustering algorithm enables to cluster agents (energy storage systems) based on their preselected feature states (local power demands and energy storage capacities). To determine the clusters, the distance of the agents’ current feature states from the average estimates of the states is determined in all clusters. The algorithm also provides average estimates of designated auxiliary states that can be used for control purposes. Presented RTDS-based real-time implementation results verify that clustering energy storage systems (batteries) into dynamic virtual power plants can reduce the network power losses.

In this letter, impedance emulation is exploited for synthesizing inertia in autonomous microgrids. An intelligent grid-forming inverter (GFI) is proposed that facilitates sufficient degrees of freedom for adaptive impedance shaping. The latter adaptively changes the effective bandwidth of the inverter's voltage controller, in response to disturbances for inertia synthesis. Deep reinforcement learning is utilized to tackle the lack of explicit quantitative relation between impedance shaping and inertia. Simulation results prove the effectiveness of the method.

Australia is one of the leading countries in energy transition, and its largest power system is intended to securely operate with up to 75% of variable renewable generation by 2025. High-inertia synchronous condensers, battery energy storage systems, and grid-forming converters are some of the technologies supporting this transformation while facilitating the secure operation of the grid. Synchronous condensers have enabled 2500 MW of solar and wind generation in the state of South Australia, reaching minimum operational demands of ≈100 MW. Grid-scale battery energy storage systems have demonstrated not only market benefits by cutting costs to consumers but also essential grid services during contingencies. Fast frequency response, synthetic inertia, and high fault currents are some of the grid-supporting capabilities provided by new developments that strengthen the grid while facilitating the integration of new renewable energy hubs. This manuscript provides a comprehensive overview, based on the Australian experience, of how power systems are overcoming expected challenges while continuing to integrate secure, low cost, and clean energy.