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With the increasing deployment of battery storage devices in residential electricity networks, it is essential that the charging and discharging of these devices be scheduled so as to avoid adverse impacts on the electricity distribution network. In this paper, we propose a non-cooperative, price-based hierarchical distributed optimisation approach that provably recovers the centralised, or cooperative, optimal performance from the point of view of the network operator. The distributed optimisation algorithm provides important insights into the appropriate design of contracts between an energy provider and their associated residential customers, who can themselves act as energy providers as well as consumers (e.g. due to rooftop solar photovoltaics and batteries) depending on the time of the day and on real-time prices. To make the presentation self-contained, and to highlight key properties of the price-based optimisation algorithm, the dual ascent method and its convergence properties are reviewed. The performance of the proposed price-based optimisation algorithm is validated on recent measurement taken from an Australian electricity distribution company, Ausgrid. In addition to analysing the results of the open loop solution, we investigate the effect of real-time prices in the closed loop using a model predictive control framework. [ABSTRACT FROM AUTHOR]

This article investigates a bi-objective scheduling problem on uniform parallel machines considering electricity cost under time-dependent or timeof- use electricity tariffs, where electricity price changes with the hours within a day. The aim is to minimize simultaneously the total electricity cost and the number of machines actually used. A bi-objective mixed-integer linear programming model is first formulated for the problem. An insertion algorithm is then proposed for the single-objective scheduling problem of minimizing the total electricity cost for a given number of machines. To obtain the whole Pareto front of the problem, an iterative search framework is developed based on the proposed insertion algorithm. Computational results on real-life and randomly generated instances demonstrate that the proposed approach is quite efficient and can find high-quality Pareto fronts for large-size problems with up to 5000 jobs. [ABSTRACT FROM AUTHOR]

A method for applying combinatorial optimization algorithms to Energy Storage System (ESS) scheduling is presented in this paper. Scheduling is essential for the integration of ESS in electrical networks at grid level or at consumer level to achieve the objectives of integration such as constraint management or energy cost reduction and for efficient storage dispatch. It also shows that for a time-of-use (ToU) tariff scheme based on the shape of the demand profile with higher prices tied to peak periods, effective load-leveling, and peak demand reduction always leads to energy cost reduction. While other methods usually require more information such as generation cost curves or ToU tariffs to schedule ESS, the proposed method uses only demand profile information and ESS parameters to achieve load-leveling and peak demand reduction and also considers the entire optimization time horizon. This is done by combining heuristic bin packing and subset sum algorithms with specific modifications to the standard forms and through transformations. A case study is presented in which the algorithm is used to schedule household ESS with repurposed electric vehicle batteries and the results are compared to a demand response scheme on the same setup. [ABSTRACT FROM AUTHOR]

The widespread proliferation of Electric Vehicles (EVs) can have a transformative effect on the electric power system. The power and energy consumed by EVs when charging is substantial, which has consequences on power system operation and planning. This paper identifies, evaluates, and proposes time-series seasonal autoregressive integrated moving average (ARIMA) models of aggregated EV charging station load. The modeling is based on 2 years of time-stamped aggregate power consumption from over 2400 charging stations in Washington State and San Diego, California. The different data sets allow the influence of time-of-use pricing on the time-series models to be explored. Weekday, weekend, and near-term and long-term models are developed and analyzed. The best performing near-term weekday models are (2, 0, 0) × (0, 1, 1)24× (1, 0, 0)120for Washington State and (2, 0, 0) × (1, 1, 0)24× (0, 0, 1)48for San Diego. Applications of the seasonal ARIMA models to aggregate EV charging station load forecasting and creation of synthetic time-series at different penetration levels are discussed. [ABSTRACT FROM PUBLISHER]

Eco-friendly energies have recently become a popular topic. Given this trend, we predict that a large number of electric vehicles (EVs) will be widely used. However, EVs need to be connected to a power system for charging, thereby causing severe risks, such as rapid increase of demand power. Therefore, in this study, we analyze the effects of EV charging on demand power, which depend on different charging schemes, namely, dumb charging, off-peak charging, time-of-use (ToU) price-based charging. For practical analysis, we conduct simulations by considering the actual power system and driving patterns in South Korea. Simulation results show that the ToU price-based charging scheme exhibits better performance in terms of demand power over the other charging schemes. [ABSTRACT FROM AUTHOR]

This research presents a method to determine the maximum potential for the capturing of solar radiation on the rooftop of buildings in an urban environment. This involves the modeling of solar energy potential and comparison to historical building energy demand profiles through the use of 3-D solar simulation software tools and geographic information systems (GIS). The objective is to accurately identify the amount of surface area that is suitable for solar photovoltaic (PV) installations and to estimate the hourly PV electricity generation potential of existing building rooftops in an urban environment. This study demonstrates a viable approach for modeling urban solar energy and offers valuable information for electricity distributors, policy makers, and urban energy planners to facilitate the substantial design of a green built environment. The developed methodology is comprised of three main sections: (1) determination of suitable rooftop area, (2) determination of the amount of incident solar radiation available per rooftop, and (3) estimation of hourly solar PV electricity generation potential. A case study was performed using this method for Ryerson University, located in Toronto, Canada. It was found that solar PV could supply up to 19% of the study area’s electricity demands during peak consumption hours. The potential benefits of solar PV was also estimated based upon hourly greenhouse gas emission intensity factors as well as Time-of-Use (TOU) savings through the Ontario Feed-in-Tariff (FIT) program, which allows for better representation of the positive impacts of solar technologies. [ABSTRACT FROM AUTHOR]

A new methodology for determining the optimal operating strategies for a chilled-water-storage system under a time-of-use electricity rate structure is introduced in Part I. In Part II, a practical project is introduced to illustrate the application of this methodology. It is is a large-scale chilled-water system with a 5.4 million gallon water storage tank and an advanced control system. The methodology proposed in this dissertation is successfully applied in this system to search the optimal operations of the thermal energy storage (TES) and chiller plant. The profiles of system cooling load and weather conditions between March 2007 and February 2008 are selected as the baseline. The utility rate structure is introduced and analyzed to define the off-peak and on-peak periods for the summer and winter months. Each component is modeled based on the trended data, and an hourly simulation is performed month by month. Six scenarios are designed and simulated to study the savings due to TES application, TES optimization, and plant optimization. The chiller chilled water leaving temperature and cooling tower approach temperature are optimized to further improve the plant performance. The simulation results show that the savings due to TES operation optimization and plant optimization could be very significant compared with the savings due to TES application. Many of these optimal control strategies have been implemented in the actual optimization performed on the system and achieved significant savings. [ABSTRACT FROM AUTHOR]

This article proposes a new methodology for determining the optimal operating strategies for a chilled-water storage system under a time-of-use electricity rate structure. It is based on a new classification of operating strategies and an investigation of multiple search paths. Each operating strategy consists of a control strategy and the maximum number of chillers running during the off-peak and on-peak periods. For each month, the operating strategy with the lowest monthly billing cost and minimal water level higher than the set-point is selected as the optimal operating strategy for the current month. A system model is developed to simulate the tank water level at the end of each time step and the system total power during each time step. This model includes six sub-models: plant, thermal energy storage tank, loop, chiller, control strategy, and nonplant power. This method considers the interaction among plant, tank, and loop, and it is easy for users to apply it to different projects, modify individual models, or implement new sub-models. The final results will be a table showing the monthly control strategy and maximal number of chillers staged-on during the off-peak and on-peak periods, an operation schedule that is easy for the operators to follow. [ABSTRACT FROM AUTHOR]

Increased demand response is essential in order to boost the effectiveness of the Swedish power market. The all-embracing installation of automatic meter reading systems enables power suppliers to introduce hourly settlements in the residential sector. The aim of the study has been to assess the impact of electricity retailers' physical and financial risk in customer segments with different heating systems as well as to estimate the potential of the electricity contract “Fixed price with the right to return” in terms of economic consequences and risk management. To that end, computer simulations using empirical data have been employed. The results show that the physical price and volume risk, which constitute the greatest risk of suppliers, are most severe in households whose main heating system consists of a geothermal heat pump and less serious in households that are less dependent on electricity for heating. Hourly settlements in the residential electricity market have proven to be advantageous to retailers from both an economic and risk reducing point of view and provide a superior opportunity for customers, and those with an electric heating installation in particular, to lower their electricity expenses substantially. [ABSTRACT FROM AUTHOR]

This article presents a new price-area-based zonal approach for the optimal location of distributed generation in pool, as well as hybrid electricity, markets. The price-based zones have been determined based on the nodal price of real power obtained at each node. The price-based zone has been determined based on the variation and magnitude of the real power nodal price at each node. The zone with a higher variation of the nodal real power price has been termed as zone 1, and that with a low variation of price has been termed as zone 2. Two new benefit indices, based on nodal price reduction and fuel cost savings, have been proposed and determined based on the location of distributed generations at load buses in the higher nodal price zone. The study has been performed for pool, as well as hybrid electricity, markets. The proposed optimal power flow based approach has been tested on an IEEE 24-bus reliability test system. [ABSTRACT FROM AUTHOR]

This paper presents two stochastic search algorithms for optimizing the contract capacities of time-of-use (TOU) rate industrial customers. The proposed genetic algorithm (CA) and evolutionary algorithm (EA) are used to search the optimal contract capacities for the TOU rate customers to minimize the summation of demand and penalty charges. Because of the global search capabilities of the CA and EA, the proposed algorithms can optimize the solutions of the complicated, nondifferentiable, optimal contract capacity problem. The proposed algorithms have been tested on a three-section TOU rate industrial customer of Taipower system and are compared with the existing method. The test results demonstrate that the proposed algorithms can efficiently optimize the contract capacities for TOU rate customers. [ABSTRACT FROM AUTHOR]

Beginning in March 1979 and continuing for 24 months, approximately 480 Southern California Edison (SCE) residential customers faced time-of-use (TOU) rates for electricity. These sample households were spread over three temperature zones and five consumption levels, and faced two definitions of the peak (off-peak) period and four peak/off-peak price ratios. To supply a reference case for subsequent comparisons to SCE's current domestic rate, a statistical control group of 120 customers facing standard rates was also selected. The two main rate groups in the experiment. Rate A, whose peak period definition is 10 A.M.-8 P.M. PST nonholiday weekdays, and Rate B, whose peak period definition is 12 P.M.-10 P.M. PST nonholiday weekdays, are always treated separately. In this article we use the 18 months of data on peak period, off-peak period (including weekends), and total consumption spanning May 1979-October 1980, thereby encompassing two complete summers and one complete winter We pool the monthly data in seasonal ANCOVA models but allow the months to have separate additive influences. Weather effects are accounted for explicitly. We conclude that there are significant conservation effects in the peak period as a result of TOU pricing. This result holds for the overall sample and in particular for high consumption groups (410-740 kWh/mo) and (>740 kWh/mo,), Some shifting of usage to the off-peak period does take place, but frequently this amount does not equal the peak period reduction, and hence overall conservation takes place. This conservation effect, when observed, is limited to roughly 5% in the overall sample, while in some cases peak period reductions in the summer range up to 19.2%. Increased interest in "direct" load management techniques such as remote air conditioner cycling and demand subscription service led us also to address the question of the impact of TOU pricing on system peak days. If customers choose to ignore even significant peak/off-peak price differentials on system peak days, though they may be few in number, the hoped for capacity savings from peak-load pricing may not materialize. The main findings are that contrary to the skepticism often expressed, TOU customers behave in a similar fashion on system peak days and on average summer weekdays. Indeed, there are several instances of even higher relative peak period savings on system peak days. [ABSTRACT FROM AUTHOR]

Comments on author Daniel F. Kohler's article on homothetic restriction of preferences. Introduction of the assumption of an homothetically separable utility function; Discussion on recent attempts to analyze the demand for electricity by time of use; Information on Kohler's random demand model and Monte Carlo experiment.

Comments on author Daniel F. Kohler's article on homothetic restriction of preferences. Discussion on complicated systems of demand equations derived from increasingly general utility functions; Information on the basis for much of contemporary applied econometric research; Analysis of an upward bias in price elasticities for time-of-use pricing.