With the rapid increase in distributed generation (DG), the issue of voltage regulation in the distribution network becomes more significant, and centralized voltage control (or active network management) is one of the proposed methods. Alternative work on intelligent distributed voltage and reactive power control of DG has also demonstrated benefits in terms of the minimization of voltage variation and violations as well as the ability to connect larger generators to the distribution network. This paper uses optimal power flow to compare the two methods and shows that intelligent distributed voltage and reactive power control of the DG gives similar results to those obtained by centralized management in terms of the potential for connecting increased capacities within existing networks

More effective energy production requires a greater penetration of storage technologies. This paper takes a looks at and compares the landscape of energy storage devices. Solutions across four categories of storage, namely: mechanical, chemical, electromagnetic and thermal storage are compared on the basis of energy/power density, specific energy/power, efficiency, lifespan, cycle life, self-discharge rates, capital energy/power costs, scale, application, technical maturity as well as environmental impact. It’s noted that virtually every storage technology is seeing improvements. This paper provides an overview of some of the problems with existing storage systems and identifies some key technologies that hold promise.

A bottom-up modeling approach is presented that uses a Markov chain Monte Carlo (MCMC) method to develop demand profiles. The demand profiles are combined with the electrical characteristics of the appliance to create detailed time-varying models of residential loads suitable for the analysis of smart grid applications and low-voltage (LV) demand-side management. The results obtained demonstrate significant temporal variations in the electrical characteristics of LV customers that are not captured by existing load profile or load model development approaches. The software developed within this work is made freely available for use by the community.

The penetration of electrical vehicles (EVs) is exponentially rising to decarbonize the transport sector resulting in the research problem regarding the future of their retired batteries. Landfill disposal poses an environmental hazard, therefore, recycling or reusing them as second-life batteries (SLBs) are the inevitable options. Reusing the EV batteries with significant remaining useful life in stationary storage applications maximizes the economic benefits while extending the useful lifetime before recycling. Following a critical review of the research in SLBs, the key areas were identified as accurate State of Health (SOH) estimation, optimization of health indicators, battery life cycle assessment including repurposing, End-Of-Life (EOL) extension techniques and significance of first-life degradation data on ageing in second-life applications. The inconsistencies found in the reviewed literature showed that the absence of degradation data from first as well as second life, has a serious impact on accurate remaining useful life (RUL) prediction and SOH estimation. This review, for the first time, critically surveyed the recent studies in the field of identification, selection and control of application-based health indicators in relation to the accurate SOH estimation, offering future research directions in this emerging research area. In addition to the technical challenges, this paper also analyzed the economic perspective of SLBs, highlighting the impact of accuracy in second-life SOH estimation and RUL extension on their projected revenue in stationary storage applications. Lack of standard business model based on future market trends of energy and battery pricing and governing policies for SLBs are identified as urgent research gaps.