Critical Peak Pricing Research Articles

Identification of reservation capacity in critical peak pricing electricity demand response program for sustainable manufacturing systems

SUMMARY Electricity demand response is considered an effective approach to balance the electricity demand and supply with existing infrastructure of generation, transmission, and distribution. A majority of existing literature on the electricity demand response has mainly centered on the commercial and residential building sectors while the application for the industrial sector is largely neglected. This paper presents a methodology for the application of a typical demand response program, Critical Peak Pricing (CPP) program, for the manufacturing enterprises. The configuration of the reservation capacity (in kilowatt, kW) in the CPP program, which plays a critical role in the cost of the final bill charge, will be identified by optimal production scheduling for the typical manufacturing systems with multiple machines and buffers. Mixed Integer Nonlinear Programming formulation is used to establish the mathematical model with the objective to minimize the electricity bill cost as well as the potential penalty cost due to the non-fulfillment of the target production. An approximate technique is introduced to find a near optimal solution, and a numerical case study is used to illustrate the effectiveness of the proposed method. Copyright © 2013 John Wiley & Sons, Ltd.

Critical peak pricing with load control demand response program in unit commitment problem

In this study, critical peak pricing with load control (CPPLC), recently announced by Federal Energy and Regulatory Commission, is investigated in a cost-emission-based unit commitment (UC) problem. In order to be easily implementable with available real market solvers, the non-linear, non-convex problem formulation is converted to multi-objective mixed integer linear programming (MMILP). The MMILP problem is then solved through a new modified e -constraint multi-objective optimisation method. Moreover, UC is applied not only to schedule the status of the generating units but also to determine both price deviations and load profile provided by CPPLC program. Finally, the conventional 10-unit test system is employed to indicate the applicability of the proposed method through several case studies.

Design of Time-Varying Rate Considering CO$_2$ Emission

Under fixed retail rates, electricity demands have no incentive to change their usage pattern. In contrast, under time-varying rates, suppliers can reflect the actual supply cost during the peak hours in retail prices enabling end-users to respond to the prices and reduce their loads or shift their loads from the high-price hours to the low-price hours. Load shifting may increase the reliability of the system, but may increase CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emission if coal-fired generators are the marginal generator used during the low- price hours. CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emission taxation is one of ISO's options to deal with the CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emission problem. In this paper, a new time-varying rate design method considering the CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emission taxation is suggested. In addition, market power problem and non-shifting elastic loads are considered. The suggested method is applied to a test market with critical peak pricing (CPP) and real-time pricing (RTP) and the results are analyzed. Also the suggested method is applied to the Korean power system and the results are analyzed.

Dynamic pricing of electricity for residential customers: the evidence from Michigan

The rollout of smart meters has enabled the provision of dynamic pricing to residential customers. However, doubts remain whether households can respond to time-varying price signals and that is preventing the full-scale rollout of dynamic pricing and the attainment of economic efficiency. Experiments are being conducted to test price responsiveness. We analyze data from a pilot in Michigan which featured two dynamic pricing rates and an enabling technology. Unlike most other pilots, it also included a group of “information only” customers who were provided information on time-varying prices but billed on standard rates. Similarly, unlike most other pilots, it also included two control groups, one of whom knew they were in the pilot and one of whom did not. This was designed to test for the presence of a Hawthorne effect. Consistent with the large body of experimental literature, we find that customers, including low-income participants, do respond to dynamic pricing. We also find that the response to critical peak pricing rates is similar to the response to peak time rebates, consistent with the finding of one prior experiment but inconsistent with the finding of two prior experiments. We also find that the “information only” customers respond to the provision of pricing information but at a substantially lower rate than the customers on dynamic pricing. We find that the response to enabling technology is muted. We do not find any evidence to suggest that a Hawthorne effect existed in this experiment.

Tariff Setting In the Indian Power Sector-An Overview

Electricity tariff setting is a primary instrument of economic regulation. Tariff provides economic signals, which determine the volume and nature of the demand and supply of power. It is not surprising therefore that a considerable portion of the power sector reform effort is expended on rationalizing tariffs. Through this paper we aim to discuss the potential for alternative ways of charging for electricity and providing concessions to improve the affordability of essential electricity use, facilitate the equitable, efficient and full recovery of the cost of supplying electricity and provide clear information to consumers regarding the impact of their electricity use. Many of the existing tariff elements have been formulated over the years as a result of available technology. In reviewing the electricity tariff structures it is therefore important to consider some of the developments in technology particularly with respect to metering which may facilitate a greater variety in tariff structures now or in the future. Keywords: tariff, availability based tariff (ABT), unscheduled interchange (UI), time-of-use tariff (ToU), critical peak pricing (CPP), real-time pricing (RTP), feed-in tariff (FiT), dynamic pricing, smart metering.

Managing the impact on the electric distribution network of rapid of electric vehicle adoption

Many electric utilities have begun piloting Electric Vehicle (EV) charging stations with a number of manufacturers, including Siemens. As a result of working with these EV charging station manufacturers, utilities have developed a good understanding of prevailing technology. While utilities consider options like selling or recommending preferred EV charging stations to new EV owners in their service territory, a trend has emerged where new EV owners purchase charging station technology without notifying the utility based on recommendations from their EV dealers. It is important for the utilities to be aware of customers in their service territories that are installing EV charging stations in their homes and businesses in order to plan for the required distribution network upgrades to serve these stations. Siemens has worked with a number of utilities to discuss their business requirements for a software solution envisioned to manage the installation and use of EV charging stations in their networks. This paper reviews both the problem these utilities are trying to solve together with their functional and integration requirements for a comprehensive EV charging station management system which can support utility programs including critical peak pricing programs, demand response, outage management, etc.

Dynamic Pricing of Electricity for Residential Customers: The Evidence from Michigan

The rollout of smart meters has enabled the provision of dynamic pricing to residential customers. However, doubts remain whether households can respond to time-varying price signals and that is preventing the full scale rollout of dynamic pricing and the attainment of economic efficiency. Experiments are being conducted to test price responsiveness. We analyze data from a pilot in Michigan which featured two dynamic pricing rates and an enabling technology. Unlike most other pilots, it also included a group of “information only” customers who were provided information on time-varying prices but billed on standard rates. And unlike all other pilots, it also included two control groups, one of whom knew they were in the pilot and one of whom did not. This was designed to test for the presence of a Hawthorne effect. Consistent with the large body of experimental literature, we find that customers, including low income participants, do respond to dynamic pricing. We also find that the response to critical-peak pricing rates is similar to the response to peak-time rebates, consistent with the finding of one prior experiment but inconsistent with the finding of two prior experiments. We also find that the “information only” customers respond to the provision of pricing information but at a substantially lower rate than the customers on dynamic pricing. We find that the response to enabling technology is muted. We do not find any evidence to suggest that a Hawthorne effect existed.

Look-Ahead Energy Management of a Grid-Connected Residential PV System with Energy Storage under Time-Based Rate Programs

This paper presents look-ahead energy management system for a grid-connected residential photovoltaic (PV) system with battery under critical peak pricing for electricity, enabling effective and proactive participation of consumers in the Smart Grid’s demand response. In the proposed system, the PV is the primary energy source with the battery for storing (or retrieving) excessive (or stored) energy to pursue the lowest possible electricity bill but it is grid-tied to secure electric power delivery. Premise energy management scheme with an accurate yet practical load forecasting capability based on a Kalman filter is designed to increase the predictability in controlling the power flows among these power system components and the controllable electric appliances in the premise. The case studies with various operating scenarios demonstrate the validity of the proposed system and significant cost savings through operating the energy management scheme.

Dynamic Pricing in a Moderate Climate: The Evidence from Connecticut

While many dynamic pricing experiments have been carried out in warm climates, few have been carried out in moderate climates. We analyze data from a pilot in New England which featured a time-of-use rate, two dynamic pricing rates and four enabling technologies. Unlike most other pilots, it included small commercial and industrial (C&I) customers in addition to including residential customers, for a total of around 2,200 customers. Using a constant elasticity of substitution model of consumer behavior, we find that customers do respond to dynamic pricing even in a moderate climate, that response to critical-peak pricing rates is higher than response to peak-time rebates, that there is virtually no response to TOU rates with an eight hour peak period and that small C&I customers are less price responsive than residential customers. We also find that some enabling technologies boost price responsiveness.

The study of CPP programs based on logarithmic model in electricity markets

Providing a suitable model for demand response programs (DRPs) in electricity market is a very important factor. In this regard considerable efforts have been carried out to model demand response in electricity market. By using DRPs some technical and economical characteristic of power system are improved. The DRPs are divided into two categories which are priced-based and incentive-based demand response programs. This paper presents a logarithmic modeling of critical peak pricing programs (CPP) as most prevalent priced-based DRPs. In this way, nonlinear behavioral characteristic of elastic loads is considered which causes to more realistic modeling of demand response to CPP rates. Numerical analysis on the load profile of the peak day of the Iranian power system in 2007 is conducted to examine the proposed model. Hence, different scenarios have also been considered. The results reveal the good performance of proposed models. The results are carried out by using MATLAB software. Keywords: Demand Response programs, Elasticity, Critical peak pricing programs, Electricity market, Iran

The study of CPP programs based on logarithmic model in electricity markets

Providing a suitable model for demand response programs (DRPs) in electricity market is a very important factor. In this regard considerable efforts have been carried out to model demand response in electricity market. By using DRPs some technical and economical characteristic of power system are improved. The DRPs are divided into two categories which are priced-based and incentive-based demand response programs. This paper presents a logarithmic modeling of critical peak pricing programs (CPP) as most prevalent priced-based DRPs. In this way, nonlinear behavioral characteristic of elastic loads is considered which causes to more realistic modeling of demand response to CPP rates. Numerical analysis on the load profile of the peak day of the Iranian power system in 2007 is conducted to examine the proposed model. Hence, different scenarios have also been considered. The results reveal the good performance of proposed models. The results are carried out by using MATLAB software.

Study of Various Electricity Load Management Models Under Campus Scale Smart Grid Environment

Electricity consumption management becomes more and more important with urban development and it provides an efficient way to prevent large scale blackout, to reduce environmental pollution as well as to extend the usage period of underground mineral resources. In this paper, we first study various intelligent electricity pricing models such as direct load control, time of use, critical peak pricing and real time pricing etc. which can shift load consumption from peak time to off-peak time. Then, we propose our campus scale demand response platform which integrates various electricity pricing models into it. In this way the electricity operator or utility can apply their specific pricing model to the users or participants who can efficiently manage their electricity consumption in home. Two types of demand response programs are illustrated with detailed working process and simulation results are also provided to show the effectiveness of demand response program.

Dynamic pricing of electricity in the mid-Atlantic region: econometric results from the Baltimore gas and electric company experiment

The Baltimore Gas and Electric Company (BGE) undertook a dynamic pricing experiment to test customer price responsiveness to different dynamic pricing options. The pilot ran during the summers of 2008 and 2009 and was called the Smart Energy Pricing (SEP) Pilot. In 2008, it tested two types of dynamic pricing tariffs: critical peak pricing (CPP) and peak time rebate (PTR) tariffs. About a thousand customers were randomly placed on these tariffs and some of them were paired with one of two enabling technologies, a device known as the Energy Orb and a switch for cycling central air conditioners. The usage of a randomly chosen control group of customers was also monitored during the same time period. In 2009, BGE repeated the pilot program with the same customers who participated in the 2008 pilot, but this time it only tested the PTR tariff. In this paper, we estimate a constant elasticity of substitution (CES) model on the SEP pilot’s hourly consumption, pricing and weather data. We derive substitution and daily price elasticities and predictive equations for estimating the magnitude of demand response under a variety of dynamic prices. We also test for the persistence of impacts across the two summers. In addition, we report average peak demand reduction for each of the treatment cells in the SEP pilot and compare the findings with those reported from earlier pilots. These results show conclusively that it is possible to incentivize customers to reduce their peak period loads using price signals. More importantly, these reductions do not wear off when the pricing plans are implemented over two consecutive summers. Our analyses reveal that SEP participants reduced their peak usages in the range of 18 to 33% in the first summer of the SEP pilot and continued these reductions in the second summer.

Household response to dynamic pricing of electricity: a survey of 15 experiments

Since the energy crisis of 2000–2001 in the western United States, much attention has been given to boosting demand response in electricity markets. One of the best ways to let that happen is to pass through wholesale energy costs to retail customers. This can be accomplished by letting retail prices vary dynamically, either entirely or partly. For the overwhelming majority of customers, that requires a change out of the metering infrastructure, which may cost as much as $40 billion for the US as a whole. While a good portion of this investment can be covered by savings in distribution system costs, about 40% may remain uncovered. This investment gap could be covered by reductions in power generation costs that could be brought about through demand response. Thus, state regulators in many states are investigating whether customers will respond to the higher prices by lowering demand and if so, by how much. To help inform this assessment, this paper surveys the evidence from the 15 most recent pilots, experiments and full-scale implementations of dynamic pricing of electricity. It finds conclusive evidence that households respond to higher prices by lowering usage. The magnitude of price response depends on several factors, such as the magnitude of the price increase, the presence of central air conditioning and the availability of enabling technologies such as two-way programmable communicating thermostats and always-on gateway systems that allow multiple end-uses to be controlled remotely. In addition, the design of the studies, the tools used to analyze the data and the geography of the assessment influence demand response. Across the range of experiments studied, time-of-use rates induce a drop in peak demand that ranges between 3 and 6% and critical-peak pricing (CPP) tariffs induce a drop in peak demand that ranges between 13 and 20%. When accompanied with enabling technologies, the latter set of tariffs lead to a reduction in peak demand in the 27–44% range.

The effect of utility time-varying pricing and load control strategies on residential summer peak electricity use: A review

Peak demand for electricity in North America is expected to grow, challenging electrical utilities to supply this demand in a cost-effective, reliable manner. Therefore, there is growing interest in strategies to reduce peak demand by eliminating electricity use, or shifting it to non-peak times. This strategy is commonly called “demand response”. In households, common strategies are time-varying pricing, which charge more for energy use on peak, or direct load control, which allows utilities to curtail certain loads during high demand periods. We reviewed recent North American studies of these strategies. The data suggest that the most effective strategy is a critical peak price (CPP) program with enabling technology to automatically curtail loads on event days. There is little evidence that this causes substantial hardship for occupants, particularly if they have input into which loads are controlled and how, and have an override option. In such cases, a peak load reduction of at least 30% is a reasonable expectation. It might be possible to attain such load reductions without enabling technology by focusing on household types more likely to respond, and providing them with excellent support. A simple time-of-use (TOU) program can only expect to realise on-peak reductions of 5%.

The Econometrics of Dynamic Pricing in a Mid-Atlantic Experiment

The Baltimore Gas & Electric Company (BGE) undertook a dynamic pricing experiment to test customer price responsiveness to different dynamic pricing options. The pilot ran during the summers of 2008 and 2009 and was called the Smart Energy Pricing (SEP) Pilot. In 2008, it tested two types of dynamic pricing tariffs: critical peak pricing (CPP) and peak time rebate (PTR) tariffs. Some thousand customers who randomly placed on these tariffs and some of them were paired with one of two enabling technologies, a device known as the Energy Orb and a switch for cycling central air conditioners. The usage of a randomly chosen control group of customers was also monitored during the same time period. In 2009, the pilot only tested the PTR tariff. In this paper, we estimate a well-known demand model on the hourly consumption, pricing and weather data that was compiled in the SEP pilot. We derive substitution and daily price elasticities and predictive equations for estimating the magnitude of demand response under a variety of dynamic prices. We also test for the persistence of impacts across the two summers. We also report average peak demand reduction for each of the treatment cells in the SEP pilot and compare the findings with those reported from earlier pilots.

Debate Response: Which Rate Designs Provide Revenue Stability and Efficient Price Signals? Let the Debate Continue!

Let's engage in further discussion that provides solutions and details, not just criticisms and assertions. Let's engage in a meaningful dialogue about the conditions where real-time pricing or critical peak pricing with decoupling or the SFV rate design with a feebate is most effective.

Residential response to critical-peak pricing of electricity: California evidence

This paper analyzes data from 483 households that took part in a critical-peak pricing (CPP) experiment between July and September 2004. Using a regression-based approach to quantify hourly baseline electric loads that would have occurred absent CPP events, we show a statistically significant average participant response in each hour. Average peak response estimates are provided for each of twelve experimental strata, by climate zone and building type. Results show that larger users respond more in both absolute and percentage terms, and customers in the coolest climate zone respond most as a percentage of their baseline load. Finally, an analysis involving the two different levels of critical-peak prices – $0.50/kWh and $0.68/kWh – indicates that households did not respond more to the higher CPP rate.

Measuring the Capacity Impacts of Demand Response

Critical peak pricing and peak time rebate programs offer benefits by increasing system reliability, and therefore, reducing capacity needs of the electric power system. These benefits, however, decrease substantially as the size of the programs grows relative to the system size. More flexible schemes for deployment of demand response can help address the decreasing returns to scale in capacity value, but more flexible demand response has decreasing returns to scale as well.

The Power of Dynamic Pricing

As the Smart Grid takes shape, it opens new vistas for change. One of those salient opportunities for change that is enabled by the Smart Grid is the pricing of electricity. By and large, existing rate designs hide the temporal variation in the cost of electricity and thereby promote over-consumption of electricity during peak times and under-consumption during off-peak times. In much of North America, the problem is especially pronounced during the top 60-100 hours of the year which may account for as much as 10-18 percent of system peak load. In order to meet this critical peak load, expensive combustion turbines are purchased and installed, raising rates for all customers. Dynamic pricing rate designs can remedy this problem and enhance economic efficiency. For that reason, they are receiving increased attention by state commissions throughout the country. California has made a major commitment to it, by approving the deploying of advanced metering infrastructure (AMI) and by establishing critical-peak pricing (CPP) rates as the default tariff for all non-residential customer classes with AMI. Other smart rate designs, such as real-time pricing, may be provided as options. To show the power of dynamic pricing, we develop a set of illustrative rates using data from a generic California utility and compute the benefits that would accrue to the state's economy from widespread deployment of these rates. While the numbers are specific to California, the process and methodology are perfectly general and should be of interest to utilities and regulatory bodies throughout North America. We develop dynamic pricing rates for four customer classes: Residential, Medium Commercial and Industrial (C&I), Large Commercial, and Large Industrial. In order to show the development of these rates, we begin with a discussion of existing rates. All the dynamic pricing rates are developed to be revenue neutral to these existing rates.