Renewable energy consumption, CO2 emissions and oil prices in the G7 countries

Demand response (DR) has an important role to play in the electricity market for maintaining the balance between supply and demand by introducing load flexibility instead of only adjusting generation levels, at almost all operational time scales. There are many players in the market who benefit from DR, like the TSO, DSOs, retailers and end-customers themselves. The recent advent of smart grid technologies advanced the integration of DR by providing the needed information and communication infrastructure to the existing grid. Available literature on DR talks about the concept and definitions of DR, possible DR models for various region-specific market structures along with few DR implementation experiences in a system with ever increasing levels of loads along with evolution of innovative technologies like renewables, micro-grids, PEVs, etc. In this paper, the available literature on DR is categorized into general concept papers and papers on DR models applicable to the wholesale or retail markets, and are presented in a precise manner.

Review of demand response under smart grid paradigm

On the determinants of renewable energy consumption: International evidence

The increasing demand for electricity and the emergence of smart grids have presented new opportunities for a home energy management system (HEMS) that can reduce energy usage. The HEMS incorporates a demand response (DR) tool that shifts and curtails demand to improve home energy consumption. This system commonly creates optimal consumption schedules by considering several factors, such as energy costs, environmental concerns, load profiles, and consumer comfort. With the deployment of smart meters, performing load control using the HEMS with DR-enabled appliances has become possible. This paper provides a comprehensive review on previous and current research related to the HEMS by considering various DR programs, smart technologies, and load scheduling controllers. The application of artificial intelligence for load scheduling controllers, such as artificial neural network, fuzzy logic, and adaptive neural fuzzy inference system, is also reviewed. Heuristic optimization techniques, which are widely used for optimal scheduling of various electrical devices in a smart home, are also discussed.

Review on Home Energy Management System Considering Demand Responses, Smart Technologies, and Intelligent Controllers

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Electricity consumption in G7 countries: A panel cointegration analysis of residential demand elasticities

An interruptible load management (ILM) scheme is proposed using dynamic optimal power flow analysis. It enables real-time selection of interruptible loads incorporating power network constraints and dynamic restriction on generation, viz. ramp-rate limits. The model provides an analytical framework for addressing several important issues associated with optimal selection of load curtailment, e.g. advance notification for load curtailment, short-term price discounts and long-term discounts on demand-charges, power factor of the load and customer-cost associated with the load to be curtailed, and power system security. A six-bus example illustrates the proposed methodology.

Interruptible load management using optimal power flow analysis

The energy sector in India claims 30% of the available investments. Moreover, oil import bills have the largest share among the total import bills. Thus, macro economic development and energy sector are highly interdependent. Where energy demand is forecasted without these linkages one cannot be sure if investments and imports required for energy sector will be available. The SImulation of MAcroeconomic scenarios (SIMA) model generates macroeconomically consistent energy scenarios from two interlinked submodels i.e. economic and energy submodels. The energy sector is a part of the non-agricultural sector but it is linked to both the agricultural and the non-agricultural sectors. These three sectors compete with each other for the available capital. In a two-step procedure, various energy economy relations are econometrically estimated and then these are solved simultaneously by feeding in the exogenous parameters (population, oil prices, etc.). The scenarios created correspond to 1991–2010. They are the Dynamics As Usual and the High Oil Price scenarios with capital required for phasing in the electricity sector. Energy-related emission levels for pollutants such as CO2, NOx and SO2 emissions are also calculated for each scenario.

Energy demand forecasts with investment constraints

An attempt is made to forecast electric load using neural networks. Neural networks represent a pattern or load shape and, in reality, perform a pattern recognition function. This pattern is based on training cases provided to the network. A strategy that uses a minimum distance measurement to identify the appropriate historical patterns of load and temperature readings is used to estimate the network weights. Most of the electrical power systems in India exhibit energy shortages. An optimization algorithm is used to periodically revise the forecasted load demand and the generating capacities to determine the electrical deficit.

Electric Load Forecasting Using Artificial Neural Networks and Deficit Management

Oil price shock has two interrelated impacts, GDP growth rate impact and energy impact, that induce a change in carbon emission level. This article seeks to find out, in the Indian context, whether the past oil shocks of the 1970s had any significant effect on carbon emission growth rate and then to simulate the impact on future GDP growth rate and carbon emissions if an oil price shock were to recur. Statistical diagnosis of the series confirms a structural break in the carbon emissions growth rate in 1975 following the 1973 oil shock. A Bayesian vector autoregressive model is developed with four variables: CO2 emission level, GDP, population, and oil price for the period 1970-1988. The carbon emissions and GDP for the period 1989-1995 are projected, given various simulated oil price scenarios. The oil price shock gives rise to a persistently high carbon emissions level with a time lag and an immediate negative impact on GDP that decreases within a short period.

Saumen Majumdar

Papers

Interruptible load management using optimal power flow analysis

Energy demand forecasts with investment constraints

Electric Load Forecasting Using Artificial Neural Networks and Deficit Management

Impacts of oil price shock on carbon emissions in India: An econometric analysis

Debt Crunch: What Does It Mean for Baseload Investment, Emissions and Prices?