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The increased penetration of distributed and volatile renewable generation requires the demand-side to be actively involved in energy balancing operations. This paper proposes a solution in which big data and machine learning methods are employed to enhance the capabilities of a Virtual Power Plant to participate and intelligently bid into a demand response energy market. The energy market being investigated...
The electricity price is an uncertain and changeable entity, mostly depends on power generating source and consumer's power demands behavior. The problem arises when all the consumers try to avail specific low price time slot to activate their power demands. It ends up with energy congestion or system destabilization. A better strategy is, to forecast a day ahead price and update it instantly, whenever...
In the new era of electrical power industry with more emphasis on green energy resources and active customer participation, the distribution utilities (DISCOMs) are being challenged. Being an important link between wholesale and retail electricity markets, these DISCOMs are exposed to risks on both sides. Under such circumstances, they are looking for new analytics to optimize operations and maximize...
Demand Response is an essential paradigm under the smart grid framework. The emergence of deregulated markets and dynamic pricing schemes have given an impetus to active participation from the load side. The demand patterns of a residential building show interesting trends that can provide valuable information for predicting the likelihood of occurrence of a particular load at a particular time. This...
Demand side response enables cost optimization for energy systems and consumers. By tradition, the target of the demand side response is to shift the loads from high price or peak-load periods to low price or low consumption periods. The economic effect is derived from reduced energy purchasing costs. This paper focuses on the possibility to provide demand side response for a wind park through direct...
This paper frames itself in the smart energy context where the power flow is controlled by price signals and elasticity models. In such a market, electricity prices dynamically vary and electricity consumers ought to respond with their electricity energy demand at specified time intervals. Initially load, and lately, price forecasting have been identified as essential technologies for market participants...
Aggregated demand response for smart grid services is a growing field of interest especially for market participation. To minimize economic and network instability risks, flexibility characteristics such as shiftable capacity must be known. This is traditionally done using lower level, end user, device specifications. However, with these large numbers, having lower level information, has both privacy...
A hybrid model for short-term forecasting of aggregated thermal loads and their load control responses is studied in this paper using field test data. Inputs include temperature measurement and forecast, measured power and control signals. The hybrid model comprises 1) partly physically based forecasting of the responses of the controlled thermal loads and the non-controlled power, and 2) forecasting...
The end consumers of Smart Grid have NO say in the ecosystem of electricity Grid. The price of electricity and infrastructure of grid have been solely governed by utility companies and government entities. One of the objectives of the smart grid is to bring consumer on board using IoT technologies. Peak demand is a major concern for government and utility. Since different neighborhood would have different...
To improve forecasting accuracy for baseline load and load impact from demand response resources, this paper develops three innovative statistical models. These models are regression spline fixed effect model, fixed effect change point model and mixed effect change point model. The models developed are applied to forecast baseline load and load impact from air conditioning cycling demand response...
This paper presents the forecasting algorithms for determining the electricity usage and operation status of residential heating, ventilating, and air conditioning (HVAC) systems. Two algorithms are presented based on what types of measured data can be received by the home energy management system (HEMS). Algorithm 1 is developed assuming only HVAC status is available to forecast the future HVAC usage...
The European Climate Change Program consists of a range of measures to fight against climate change, one of which is to reach 20% of renewable energy in the total energy supply in the EU by 2020. The increasing penetration of RES and DG introduces new business cases which require innovative ICT tools and the support of appropriate communication infrastructure. This paper focuses on the Virtual Power...
Independent system operators (ISOs) and utilities have begun to realize the benefits of relying on demand response (DR) for service procurement. However, an aggregating entity in the command and contracting architecture should be included to provide the ISO flexibility in scheduling units. Demand baseline establishment is a key aspect in DR programs. Real-time updating techniques that account for...
This paper describes a Neural Network application to reduce the computational complexities in a smart grid environment by predicting peak demand and losses for the next operating day. Mitigating peak demands and losses could significantly reduce outage risks and increase costs savings, hence improving the reliability and efficiency of Electrical Service Providers (ESPs).
There is increasing penetration of renewable generation in buildings and districts. There are challenges in making the effective use of this generation. The objective of the ORIGIN project (Orchestration of Renewable Integrated Generation In Neighborhoods) is to shape loads so that the fraction of energy consumed that is from local renewable generation is maximized, and energy imported from outside...
The roll out of smart meters introduces “Time of Use” tariffs to incentive demand response for household customers. This paper describes a methodology to identify the impact of demand response in customer load profiles and applies it to a smart meter data set. The smart meter data for residential household is from the Irish CER Smart Metering Project. The profiles are segmented via kmeans clustering...
Photovoltaic (PV)-assisted charging station is one of important charging facilities served for Electric Vehicles (EV). To minimize the operation cost of photovoltaic (PV)-assisted electric vehicle (EV) charging station, an energy management considering demand response (DR) strategy is proposed. The wavelet neural network (WNN) is utilized to forecast the price based on history data and the forecasting...
In this paper, the development of electricity price and demand forecasting, with the emergence of demand response programs, is investigated. Short Term Load/Price Forecasting (STL/PF) is performed for an electricity market that offers Demand Response (DR) Programs. The change in the forecasting errors, of both electricity price and demand, over years of inactive and active DR is monitored. Commonly...
This work seeks to determine the potentials of a Demand Aggregator into the Demand Response scheme. The authors describe and validate the optimization technique used by the Aggregator to enable demand flexibility in domestic microgrid premises. The microgrid is comprised of Distributed Generation and shiftable load devices. By applying a monetary incentive signal in the microgrid's Energy Management...
The intense environmental concern regarding the CO2 footprint of the energy sector entails the higher Renewable Energy Sources (RES) share in the energy mix of the global production profile. Currently, energy is produced and distributed under a centralized framework preventing small electricity producers from participating in the electricity market, unless they are part of larger energy associations,...
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