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Group-Based Pricing to Shape Demand in Real-Time Electricity Markets
Abstract
Maintaining the balance between electricity supply and demand is one of the major concerns of utility operators. With the increasing contribution of renewable energy sources in the typical supply portfolio of an energy provider, volatility in supply is increasing while the control is decreasing. Real time pricing based on aggregate demand, unfortunately cannot control the non-linear price sensitivity of deferrable/flexible loads and leads to other peaks [4, 5] due to overly homogenous consumption response. In this paper, we present a day-ahead group-based real-time pricing mechanism for optimal demand shaping. We use agent-based simulations to model the system-wide consequences of deploying different pricing mechanisms and design a heuristic search mechanism in the strategy space to efficiently arrive at an optimal strategy. We prescribe a pricing mechanism for each groups of consumers, such that even though consumption synchrony within each group gives rise to local peaks, these happen at different time slots, which when aggregated result in a flattened macro demand response. Simulation results show that our group-based pricing strategy out-performs traditional real-time pricing, and results in a fairly flat peak-to-average ratio.
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Regulating the power consumption to avoid peaks in demand is a known method. Demand Response is used as tool by utility providers to minimize costs and avoid network overload during peaks in demand. Although it has been used extensively there is a shortage of solutions dealing with real-time scheduling of DR events. Past attempts focus on minimizing the load demand while not dealing with the uncertainty induced by customer intervention which hinder sustainability of the reduced load. In this paper we describe a smart selection algorithm that solves the problem of scheduling DR events in a broad spectrum of customers observed in common Smart Grid Infrastructures. We deal both with the problem of real-time operation and sustainability of the reduced load while factoring customer comfort levels. Real Data were used from the USC campus micro grid in our experiments. On the overall achievable reduction the results produced a maximum average approximation error ≈ 0.7%. Sustainability of the targeted load was achieved with maximum average error of less than 3%. It is also shown that our solution fulfils the requirements for Dynamic Demand Response providing a solution in a reasonable amount of time.
Demand Response(DR) is a common practice used by utility providers to regulate energy demand. It is used at periods of high demand to minimize the peak to average consumption ratio. Several methods have been proposed over the previous years on how to formulate and deal with the problem of excess demand. Following these methods automated systems for initiating and regulating demand response events have emerged. In this paper we present an automated system for providing an estimated demand response schedule of participating customers. We quantify the reduction using information about the baseline consumption and the consumption during DR. Our goal is to provide a sustainable reduction to ensure the elimination of peaks in demand. We also provide a mechanism to deal with real time DR adaptation when our prediction is more optimistic than the actual achieved reduction. We provide the results from a series of experiments comparing the achieved reduction to the predicted one as well as the sustainability of the provided solution.
A number of states and utilities are pursuing demand response based on dynamic and time-differentiated retail prices and utility investments in Advanced Metering Infrastructure (AMI), often as part of Smart Grid initiatives. These developments could produce large amounts of Price Responsive Demand, demand that predictably responds to changes in wholesale prices. Price Responsive Demand could provide significant reliability and economic benefits. However, existing RTO tariffs present potential barriers to the development of Price Responsive Demand. Effectively integrating Price Responsive Demand into RTO markets and operations will require changes in demand forecasting, scarcity pricing reform, synchronization of scarcity pricing with capacity markets, tracking voluntary hedging by price responsive loads, and a non-discriminatory approach in curtailments in capacity emergencies. The article describes changes in RTO policies and systems needed incorporate Price Responsive Demand.
This paper aims at defining a dynamic and flexible tariff structure for a distribution company that protects the retail consumers
against the excessive fluctuations of the wholesales market prices. We propose a two-stage pricing scheme that sets in a first-stage
a time-of-use tariff that is corrected later by a dynamic component once the real-time demand has been observed. A personalized
tariff scheme may be offered by a distribution company to each dynamic customer by allowing him to choose the appropriate
robustness level expressed in terms of variability between the first and the second-stage decisions. The arising limited recourse
model has been tested on realistic test problems, by using a slight modification of a recently proposed interior point solution
framework.
Central to the vision of the smart grid is the deployment of smart meters that will allow autonomous software agents, representing the consumers, to optimise their use of devices and heating in the smart home while interacting with the grid. However, without some form of coordination, the population of agents may end up with overly-homogeneous optimised consumption patterns that may generate significant peaks in demand in the grid. These peaks, in turn, reduce the efficiency of the overall system, increase carbon emissions, and may even, in the worst case, cause blackouts. Hence, in this paper, we introduce a novel model of a Decentralised Demand Side Management (DDSM) mechanism that allows agents, by adapting the deferment of their loads based on grid prices, to coordinate in a decentralised manner. Specifically, using average UK consumption profiles for 26M homes, we demonstrate that, through an emergent coordination of the agents, the peak demand of domestic consumers in the grid can be reduced by up to 17% and carbon emissions by up to 6%. We also show that our DDSM mechanism is robust to the increasing electrification of heating in UK homes (i.e. it exhibits a similar efficiency).
In this paper, we present a directly applicable scheme for electricity consumption shifting and effective demand curve flattening. The scheme can employ the services of either individual or cooperating consumer agents alike. Agents participating in the scheme, however, are motivated to form cooperatives, in order to reduce their electricity bills via lower group prices granted for sizable consumption shifting from high to low demand time intervals. The scheme takes into account individual costs, and uses a strictly proper scoring rule to reward contributors according to efficiency. Cooperative members, in particular, can attain variable reduced electricity price rates, given their different load shifting capabilities. This allows even agents with initially forbidding shifting costs to participate in the scheme, and is achieved by a weakly budget-balanced, truthful reward sharing mechanism. We provide four variants of this approach, and evaluate it experimentally. Copyright © 2013, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
This paper investigates how critical-peak pricing (CPP) affects households with different usage and income levels, with the goal of informing policy makers who are considering the implementation of CPP tariffs in the residential sector. Using a subset of data from the California Statewide Pricing Pilot of 2003–04, average load change during summer events, annual percent bill change, and post-experiment satisfaction ratings are calculated across six customer segments, categorized by historical usage and income levels. Findings show that high-use customers respond significantly more in kW reduction than do low-use customers, while low-use customers save significantly more in percentage reduction of annual electricity bills than do high-use customers—results that challenge the strategy of targeting only high-use customers for CPP tariffs. Across income levels, average load and bill changes were statistically indistinguishable, as were satisfaction rates—results that are compatible with a strategy of full-scale implementation of CPP rates in the residential sector. Finally, the high-use customers earning less than $50,000 annually were the most likely of the groups to see bill increases—about 5% saw bill increases of 10% or more—suggesting that any residential CPP implementation might consider targeting this customer group for increased energy efficiency efforts.
Real-time electricity pricing models can potentially lead to economic and environmental advantages compared to the current common flat rates. In particular, they can provide end users with the opportunity to reduce their electricity expenditures by responding to pricing that varies with different times of the day. However, recent studies have revealed that the lack of knowledge among users about how to respond to time-varying prices as well as the lack of effective building automation systems are two major barriers for fully utilizing the potential benefits of real-time pricing tariffs. We tackle these problems by proposing an
optimal
and
automatic
residential energy consumption scheduling framework which attempts to achieve a desired trade-off between minimizing the
electricity payment
and minimizing the
waiting time
for the operation of each appliance in household in presence of a real-time pricing tariff
combined
with
inclining block rates
. Our design requires minimum effort from the users and is based on simple
linear programming
computations. Moreover, we argue that any residential load control strategy in real-time electricity pricing environments requires
price prediction
capabilities. This is particularly true if the utility companies provide price information only one or two hours ahead of time. By applying a simple and efficient
weighted average price prediction
filter to the actual hourly-based price values used by the Illinois Power Company from January 2007 to December 2009, we obtain the optimal choices of the
coefficients
for each day of the week to be used by the price predictor filter. Simulation results show that the combination of the proposed energy consumption scheduling design and the price predictor filter leads to significant reduction not only in users' payments but also in the resulting
peak-to-average ratio
in load demand for various load scenarios. Therefore, the deployment of the proposed optimal energy consumption scheduling schemes is beneficial for both end users and utility companies.
Central to the vision of the smart grid is the deployment of smart meters that will allow autonomous software agents, representing the consumers, to optimise their use of devices and heating in the smart home while interacting with the grid. However, without some form of coordination, the pop- ulation of agents may end up with overly-homogeneous op- timised consumption patterns that may generate significant peaks in demand in the grid. These peaks, in turn, reduce the efficiency of the overall system, increase carbon emis- sions, and may even, in the worst case, cause blackouts. Hence, in this paper, we introduce a novel model of a De- centralised Demand Side Management (DDSM) mechanism that allows agents, by adapting the deferment of their loads based on grid prices, to coordinate in a decentralised man- ner. Specifically, using average UK consumption profiles for 26M homes, we demonstrate that, through an emergent co- ordination of the agents, the peak demand of domestic con- sumers in the grid can be reduced by up to 17% and carbon emissions by up to 6%. We also show that our DDSM mech- anism is robust to the increasing electrification of heating in UK homes (i.e., it exhibits a similar efficiency).