Achieving energy flexibility is becoming a key concern for energy system planners that manage intermittent and variable generations. Industries have enormous potential to deliver large-scale energy flexibility through demand response (DR) programs. This industrial demand flexibility achieved through the demand response programs will enable widespread adoption of renewable sources in the electricity grid network. This paper aims to provide a comprehensive review of demand response and it’s industrial application by addressing: 1) Current research status, 2) Current stages of demand response applications in industries, and 3) Barriers in the deployment of DR programs. This study shows that there is significant research progress in recent years in the field of DR. It also shows potential applications of DR programs in industries. However, the study found several technical, policy, and financial barriers still exist, limiting the widespread adoption of DR. Thus, this paper offers recommendations on technical, policy, and financial measures needed to over-come the barriers and help facilitate the utilization of demand response potential, especially in industries.

Electric vehicles (EVs) have received massive consideration in the automotive industries due to their improved performance, efficiency and capability to minimize global warming and carbon emission impacts. The utilization of EVs has several potential benefits, such as increased use of renewable energy, less dependency on fossil-fuel-based power generations and energy-storage capability. Although EVs can significantly mitigate global carbon emissions, it is challenging to maintain power balance during charging on-peak hours. Thus, it mandates a comprehensive impact analysis of high-level electric vehicle penetration in utility grids. This paper investigates the impacts of large-scale EV penetration on low voltage distribution, considering the charging time, charging method and characteristics. Several charging scenarios are considered for EVs’ integration into the utility grid regarding power demand, voltage profile, power quality and system adequacy. A lookup-table-based charging approach for EVs is proposed for impact analysis, while considering a large-scale integration. It is observed that the bus voltage and line current are affected during high-level charging and discharging of the EVs. The residential grid voltage sag increases by about 1.96% to 1.77%, 2.21%, 1.96 to 1.521% and 1.93% in four EV-charging profiles, respectively. The finding of this work can be adopted in designing optimal charging/discharging of EVs to minimize the impacts on bus voltage and line current.

Data security of present-day power systems, such as the electricity market, has spurred global interest in both industry and academia. The electricity market can either be regulated (state-controlled entrance, policies, and pricing) or deregulated (open for competitors). While the security threats in a deregulated electricity market are commonly known and have been investigated for years, those in a regulated market still have scope for extensive research. Our current work focuses on exploring the data security of the regulated electricity market, and the regulated New Zealand Electricity Market (NZEM) has been considered for this research. Although the chances of cyberattacks on state-controlled regulated electricity market are relatively less, different layers of the current SCADA systems do pose some threats. In this context, we propose a decentralized Ethereum Blockchain-based end-to-end security prototype for a regulated electricity market such as the NZEM. This prototype aims to enhance data security between the different layers of the current SCADA systems. The detailed operation process and features of this prototype are presented in this work. The proposed prototype has prospects of offering improved data security solutions for the regulated electricity market.

There is an increase in renewable energy sources connected to the electricity grid due to recent drives to achieve grid decarbonization milestones. However, such expansions cause grid balancing issues due to the renewable sources intermittency. Thus, grid operators introduced demand response (DR) schemes to mitigate this problem by controlling consumer load demands in exchange for incentives. Industries have enormous electricity demand making them ideal candidates for such programs. Nonetheless, non-intrusive demand load flexibility assessment for an industry's potential in DR programs remains a challenge. In this paper, a data-driven framework for quantifying the DR potential of an industrial consumer is proposed. The framework uses smart electricity meter data to identify operational patterns to derive a flexibility boundary that quantifies the flexibility in the industrial consumer's system. The framework also evaluates the DR participation scenario to quantify the net benefit of trading the identified flexibility. A Case-study has been carried out for two industrial consumers (i.e., an electronics factory and a poultry feed factory). Initial energy behavioral analysis indicates three different energy use patterns for the electronics factory and six energy use patterns for the poultry feed factory. Evaluating the operational flexibility boundary for the clusters, the framework found two feasible clusters with DR potentials for the electronics factory and three feasible clusters for the poultry feed factory. The cost-benefit analysis indicates a potential energy cost reduction in the region of $5\%-8\%$ for passive participation and as much as $12\%-24\%$ for active participation. The framework could be adopted to evaluate wide scale industrial consumers' flexibility potential.

Growing demand for electricity and the innovations in smart grid technologies is providing new opportunities for intelligent electrical demand loads control in the form of Demand Response (DR). In this paper, we present a comprehensive review of demand response by conducting a systematic mapping of the field to evaluate the current research trends, knowledge gaps, and the scope of future research of DR in the smart grid applications. Bibliometric analysis has been done to map the research themes under the demand response domain by analyzing the co-relationships in the metadata originated from the SCOPUS database. Thorough content analysis has also been performed, and six emerging research themes have been identified along with fundamental research questions commonly addressed to devise future research directions.