Peak Load Hours Research Articles

A Strategy to Enhance Electric Vehicle Penetration Level in India

Abstract In wake of the very recent initiative by the Indian government under the Faster Adoption and Manufacturing of Hybrid and Electric Vehicle (FAME) scheme 2015, to provide subsidies for electric vehicle purchase, the cost difference between electric vehicle and conventional diesel vehicle is bridged to an extent. This paper attempts to provide strategies to increase the cost advantage of Electric vehicles by reducing its payback period by exploring possibility of vehicle to home(V2H) scheme and thus to increase the willingness to pay (WTP) of the customer. The biggest beneficiary of this scheme will be the Indian power grid. This scheme will enable capacity enhancement during peak load hours in the Indian scenario utilizing the stored energy available in the electric vehicle using V2H system. Electric vehicle can be used as a storage methodology i.e. when electric vehicles are parked and connected to a charger, it acts as a storage space for electrical energy. An Indian 11 kV distribution network is used to conduct the case study, to illustrate the effectiveness of the developed concept for reducing peak demand. The hypothesis presented by the author is specific for a State, nevertheless these findings are heterogeneous enough across the country and this can significantly increase social benefits for the whole nation. Overall results indicate that implementation of V2H can go a long way in addressing two of the present major technological problems of cost disadvantage of electric vehicle and peak levelling of Indian power grid by identifying and exploring potential cost benefit avenues to reduce the payback period of EV's and thereby making them a technology of our everyday life rather than an idealistic plan of a distant future.

Autonomous Demand Response Using Stochastic Differential Games

Demand response (DR) programs are implemented to encourage consumers to reduce their electricity demand when needed, e.g., at peak-load hours, by adjusting their controllable load. In this paper, our focus is on controllable load types that are associated with dynamic systems and can be modeled using differential equations. Examples of such load types include heating, ventilation, and air conditioning; water heating; and refrigeration. In this regard, we propose a new DR model based on a two-level differential game framework. At the beginning of each DR interval, the price is decided by the upper level (aggregator, utility, or market) given the total demand of users in the lower level. At the lower level, for each player (residential or commercial buildings that are equipped with automated load control systems and local renewable generators), given the price from the upper level, the electricity usage of air conditioning unit, and the battery storage charging/discharging schedules, are controlled in order to minimize the user's total electricity cost. The optimal user strategies are derived using the stochastic Hamilton-Jacobi-Bellman equations. We also show that the proposed game can converge to a feedback Nash equilibrium. Based on the effect of real-time pricing on users' daily demand profile, the simulation results demonstrate the properties of the proposed game and show how we can optimize consumers' electricity cost in the presence of time-varying prices.

Thermo-economic Optimization of Solar Assisted Heating and Cooling (SAHC) System

The energy demand for cooling is continuously increasing due to growing thermal loads, changing architectural modes of building, and especially due to occupants indoor comfort requirements resulting higher electricity demand notably during peak load hours. This increasing electricity demand is resulting higher primary energy consumption and emission of green house gases (GHG) due to electricity generation from fossil fuels. An exciting alternative to reduce the peak electricity consumption is the possible utilization of solar heat to run thermally driven cooling machines instead of vapor compression machines utilizing high amount of electricity. In order to widen the use of solar collectors, they should also be used to contribute for sanitary hot water production and space heating. Pakistan lying on solar belt has a huge potential to utilize solar thermal heat for heating and cooling requirement because cooling is dominant throughout the year and the enormous amount of radiation availability provides an opportunity to use it for solar thermal driven cooling systems. The sensitivity analysis of solar assisted heating and cooling system has been carried out under climatic conditions of Faisalabad (Pakistan) and its economic feasibility has been calculated using maximization of NPV. Both storage size and collector area has been optimized using different economic boundary conditions. Results show that optimum area of collector lies between 0.26m2 to 0.36m2 of collector area per m2 of conditioned area for ieff values of 4.5% to 0.5%. The optimum area of collector increases by decreasing effective interest rate resulting higher solar fraction. The NPV was found to be negative for all ieff values which shows that some incentives/subsidies are needed to be provided to make the system cost beneficial. Results also show that solar fraction space heating varies between 87 and 100% during heating season and solar fraction cooling between 55 and 100% during cooling season which indicates a huge amount of conventional energy saving potential.

Optimal Virtual Power Plant Dispatching Approach

As the integrator of energy resources (DERs), a virtual power plant (VPP) would be able to control the amount of the power access to the distribution transformers such that energy efficiency can be improved. Battery energy storage system (BESS) and demand response (DR) as DERs can entrust the VPP with certain controllability to regulate the power supply of the distribution system. This paper aims to maximize the benefit of the supplied powers over the 24 hours under VPP operation. Combining an iterative dynamic programming optimal BESS schedule approach and a PSO-based DR scheme optimization approach, an optimal VPP operational method is proposed to minimize the total electricity cost with respect to the power supply limit of the distribution transformers and the system security constraints, especially, within the peak load hours. With the TOU rate given each hour, test results had confirmed the validity of the proposed method with the obviously decreased power supply in each peak-load hours and the largely reduced electricity cost accordingly.

Enhanced multi-dimensional power network planning based on ant colony optimization

SUMMARY One ultimate goal of power network planning is to construct power systems that are environmentally friendly. However, it is still a challenging issue to efficiently consider environmental factors in power network planning problems due to the complexity and the diversity of environment. This paper presents a nonlinear approach to formulate the multi-dimensional spatial power network planning problem based on raster map in geographic information systems. With the objective of minimizing investment and operation costs, the proposed model integrates five dimensions: the substation location, the substation sizing, the line type selection, the optimal electric line routing, and the power network evaluation in terms of the ac power flow calculation. Furthermore, the branch capacity constraints and the node voltage limitations at peak load hours are examined to ensure the operational security of power systems. To overcome difficulties in solving the mixed-integer nonlinear optimization problem, an enhanced ant colony optimization with the four-search-space structure and the improved state transition rule is introduced. The case results validate the proposed model and the algorithms. Copyright © 2014 John Wiley & Sons, Ltd.

Parallel Load Schedule Optimization With Renewable Distributed Generators in Smart Grids

We propose a framework for demand response in smart grids that integrates renewable distributed generators (DGs). In this model, some users have DGs and can generate part of their electricity. They can also sell extra generation to the utility company. The goal is to optimize the load schedule of users to minimize the utility company's cost and user payments, while considering user satisfaction. We employ a parallel autonomous optimization scheme, where each user requires only the knowledge of the aggregated load of other users, instead of the load profiles of individual users. All the users can execute distributed optimization simultaneously. The distributed optimization is coordinated through a soft constraint on changes of load schedules between iterations. Numerical examples show that our method can significantly reduce the peak-hour load and costs to the utility and users. Since the autonomous user optimization is executed in parallel, our method also significantly decreases the computation time and communication costs.

Analysis of PV-FC Hybrid System Operation Considering Sale Electricity

This paper presents a hybrid power generation system modeling and simulation with the objective of electricity sale to distribution network (DN) which consists of photovoltaic (PV) module, proton exchange membrane (PEM) fuel cell (FC), hydrogen storage tank (HST) and electrolyzer (EL).Since last researches in optimal FC and PV application aimed in power electronic approach, In this paper the application between FC and PV is considered with the aim of maximizing profit gained due to electricity sale revenue to DN. The revenue from electricity sale to DN considering electricity price in low load, shoulder load and peak load hours is considered as the system profit. Also in a sensitivity analysis the impact of technical parameters of hybrid system components is investigated on system profit. The results showed that the system saves the electricity by hydrogen storage in HST in low load hours and sale it with more prices in shoulder load hours to DN. Also the obtained results show that several technical parameters of PV and PEM FC have considerable impact on system operation and profit. Index Terms—Photovoltaic, Fuel cell, Electricity Price, Distribution network, GAMS

Sensing-Performance Tradeoff in Cognitive Radio Enabled Smart Grid

Smart grid is widely considered to be the next generation of power grid, where power generation, management, transmission, distribution, and utilization are fully upgraded to improve agility, reliability, efficiency, security, economy, and environmental friendliness. Demand response management (DRM) is recognized as a control unit of the smart grid, with the attempt to balance the real-time load as well as to shift the peak-hour load. Communications are critical to the accuracy and optimality of DRM, and hence at the core of the control performance of the smart grid. In this paper, we introduce cognitive radio into the smart grid to improve the communication quality. By means of spectrum sensing and channel switching, smart meters can decide to transmit data on either an original unlicensed channel or an additional licensed channel, so as to reduce the communication outage. Considering the energy cost taxed by spectrum sensing together with the control performance degradation incurred by imperfect communications, we formulate the sensing-performance tradeoff problem between better control performance and lower communication cost, paving the way towards a green smart grid. The impact of the communication outage on the control performance of DRM is also analyzed, which reduces the profit of power provider and the social welfare of the smart grid, although it may not always decrease the profit of power consumer. By employing the energy detector, we prove that there exists a unique optimal sensing time which yields the maximum tradeoff revenue, under the constraint that the licensed channel is sufficiently protected. Numerical results are provided to validate our theoretical analysis.

Demand Response Optimization for Smart Home Scheduling Under Real-Time Pricing

Demand response (DR) is very important in the future smart grid, aiming to encourage consumers to reduce their demand during peak load hours. However, if binary decision variables are needed to specify start-up time of a particular appliance, the resulting mixed integer combinatorial problem is in general difficult to solve. In this paper, we study a versatile convex programming (CP) DR optimization framework for the automatic load management of various household appliances in a smart home. In particular, an <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularization technique is proposed to deal with schedule-based appliances (SAs), for which their on/off statuses are governed by binary decision variables. By relaxing these variables from integer to continuous values, the problem is reformulated as a new CP problem with an additional <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularization term in the objective. This allows us to transform the original mixed integer problem into a standard CP problem. Its major advantage is that the overall DR optimization problem remains to be convex and therefore the solution can be found efficiently. Moreover, a wide variety of appliances with different characteristics can be flexibly incorporated. Simulation result shows that the energy scheduling of SAs and other appliances can be determined simultaneously using the proposed CP formulation.

Simultanous emergency demand response programming and unit commitment programming in comparison with interruptible load contracts

Different reasons such as power system restructuring, electricity price variation in some hours of a day and growth in the fuel price, have led to more attention to demand response (DR) programmes for consumers. Two important issues in DR programmes are emergency demand response programming (EDRP) and Interruptible/Curtail able (I/C) programme that is utilised as interruptible load contracts (ILC). The EDDR is utilised to decrease the consumption in peak load or critical hours of the day. This occurs by means of customer&apos;s behaviour in response to the incentives. However, the curtailment of voluntarily loads in the critical hours considering the consumer requirements is an action that can be performed in DR. A model has been proposed for EDRP and ILC implementation in the unit commitment (UC) programme. Then, the effect of these two DR programmes have been compared in long-term UC problem with fuel constraints. The proposed methodology has been formulated as a mixed integer linear problem and implemented in GAMS environment. The proposed model is applied to a 6-bus test system and a modified IEEE 118-bus system to demonstrate its effectiveness.

The role of demand-side management in the grid integration of wind power

In a scenario of large scale penetration of renewable production from wind and other intermittent resources, it is fundamental that the electric system has appropriate means to compensate the effects of the variability and randomness of the wind power availability. This concern was traditionally addressed by the promotion of wind resource studies and acting in the supply side of energy and using energy storage technologies. However, in electric system planning, other options deserve to be evaluated, namely the options related with the energy demand. The most severe problems due to the wind power intermittence happen during the peak load hours. Thus, instead of trying to replace the lost capacity due to the intermittence, other option is to act in the energy demand side, with the aim of reducing the consumption in such hours. The demand-side management technologies are an option that must be considered to reduce and manage the wind power intermittence. The present paper analyzes the possible impact of demand-side management and demand response, with the aim of enabling the integration of the growing intermittent resources in Portugal.

Effects of Temporal Wind Patterns on the Value of Wind-Generated Electricity in California and the Northwest

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Wind power production is variable, but also has diurnal and seasonal patterns. These patterns differ between sites, potentially making electric power from some wind sites more valuable for meeting customer loads or selling in wholesale power markets. This paper investigates whether the timing of wind significantly affects the value of electricity from sites in California and the Northwestern United States. We use both measured and modeled wind data and estimate the time-varying value of wind power with both financial and load-based metrics. We find that the potential difference in wholesale market value between better-correlated and poorly correlated wind sites is modest, on the order of 5%–10%. A load-based metric, power production during the top 10% of peak load hours, varies more strongly between sites, suggesting that the capacity value of different wind projects could vary by as much as 50% based on the timing of wind alone. </para>

Part two: Control of a novel HTPEM-based micro combined heat and power fuel cell system

A novel micro combined heat and power system and a dynamic model thereof were presented in part one of the publication. In the following, the control system and dynamic performance of the system are presented. The model is subjected to a measured consumption pattern of 25 Danish single family houses with measurements of heat, power and hot water consumption every 15th minute during one year. Three scenarios are analyzed ranging from heat following only (grid compensation for electricity) to heat and power following with net export of electricity during high and peak load hours. Average electrical efficiencies above 40 % LHV and beyond 90 % LHV total efficiency were obtained.

UNDERSTANDING THE IMPORTANCE OF AEROBIC MIXING, BIOFILM THICKNESS CONTROL AND MODELING ON THE SUCCESS OR FAILURE OF IFAS SYSTEMS FOR BIOLOGICAL NUTRIENT REMOVAL

Research was conducted in IFAS systems to identify problems that arose with the biofilm when the intensity of mixing and aeration fall below or rise above certain thresholds. The types of IFAS media evaluated include fixed bed cord media (Ringlace and Bioweb), moving bed sponge (sponge media – Linpor and Captor; and plastic media – Kaldnes) and RBC media. An aeration process design model and operating strategies have been developed to address these problems. This paper presents the IFAS and MBBR design methodology and aeration process design component of the Aquifas Unified Model for Activated Sludge, IFAS and MBBR systems. The paper references specific instances in full-scale IFAS plants where problems occurred because of insufficient or improper mixing patterns and documents how they were resolved. The problems observed included nuisance predator development and plugging of media and screens. The IFAS model was upgraded to incorporate computation of biofilm thickness and the impact of the increase in biofilm thickness on the reduction in effective surface area available on a various types of media. Effective surface area is the m 2 of biofilm surface per m 3 of tank volume. The model uses the effective surface area and the amount of media in the tank to compute the substrate (COD, NH4N and Oxidized-N) profiles. Additional research is being conducted to establish certain thresholds for mixing necessary above and beyond the aeration requirements to maintain a thin biofilm. The paper shows how the process design model is applied to improve the design of IFAS and MBBR systems. In instances where the aeration requirement to satisfy the DO set-point is satisfied but the mixing is below the threshold to maintain a thin biofilm, the media fill volume fraction in an aerobic cell can be increased and the fraction of tank volume occupied by the aerobic cell can be increased. By increasing the media fill volume fraction in a situation where the biofilm in an aerobic cell is too thick, the total amount of biofilm surface area present in the aerobic cell is increased and the soluble COD concentration is decreased. This decreases the biofilm thickness observed for the same intensity of mixing and increases the effective surface area of biofilm. This improves the performance of the system. When the media fill volume fraction is increased, the air flow per unit of reactor volume has to be increased to satisfy the additional oxygen demand. This increase in air flow helps satisfy the mixing requirements to mix the media and induce the requisite amount of biofilm shear. In some instances with fixed bed media, supplemental aeration system is installed below the frames to increase the air flow during diurnal peak load hours and thin out the biofilm.

Economic effects of re-allocating airports slots: a vertical differentiation approach

The promotion of competition policies in air transport faces several obstacles, one being the absence of mobility of airports’ slots. The European Commission has been preparing legislation on this issue as a springboard to a more efficient use of scarce airport capacity. This paper analyses possible outcomes of such a policy, using a model that captures the main features of the airport market before and after the re-allocation of slots. Results show that the achievement of more efficiency depends on the criteria used for its assessment. In particular, welfare falls after the process of re-allocation. An empirical test of airlines’ pricing strategies is presented, and confirms one of the model's results, i.e. that airlines differentiate more prices between peak load and off peak hours in a more competitive environment.

Don't let the sun go down on PV

In response to global issues for the generation and delivery of electrical power, photovoltaic (PV) and other renewable energy technologies are gaining acceptance as a way of maintaining and improving living standards without harming the environment. PV could help mitigate energy crises by reducing peak demand and securing the delivery of power in congested locations by decentralizing the power generation. This article shows the challenges of photovoltaic in restructured power systems. PV technologies could provide more secure mode of operation for power system during peak load hours when the systems are constrained and locational marginal prices are high.

A Comparison Between Chronological and Probabilistic Methods to Estimate Wind Power Capacity Credit

In this paper, the issue of the capacity credit provided by wind energy conversion systems (WECS) is addressed. A chronological method of post-evaluation of the capacity credit is presented and compared with a pre-evaluation probabilistic method. The proposed chronological approach is based on the computation of the WECS capacity factor (ratio between average and total output) over some relevant time period. An appropriate choice of the time interval, for instance the peak load hours, will lead to a closer approximation of the capacity credit. The comparison is illustrated with two case-studies, concerning the Portuguese electric system. The analysis of the theoretical background of both methods and the results obtained allow the conclusion that chronological methods are best designed to assist system operators, whereas probabilistic methods, which are developed within the logic of the public system avoided resources, are a helping tool for system planners.

Demonstration of the environmental and demand-side management benefits of grid-connected photovoltaic power systems

This study investigated the pollutant emission reduction and demand-side management potential of 16 photovoltaic (PV) systems installed across the US during 1993 and 1994. The US Environmental Protection Agency (EPA) and 11 electric power companies sponsored the project. This article presents results of analyses of each PV system's ability to offset power plant emissions of sulfur dioxide (SO 2), nitrogen oxides (NO x ), carbon dioxide (CO 2) and particulates and to provide power during peak demand hours for the individual host buildings and peak load hours for the utility. The analyses indicate a very broad range in the systems' abilities to offset pollutant emissions, due to variation in the solar resource available and the emission rates of the participating utilities' load following generation plants. Each system's ability to reduce building peak demand was dependent on the correlation of that load to the available solar resource. Most systems operated in excess of 50% of their capacity during building peak load hours in the summer months, but well below that level during winter peak hours. Similarly, many systems operated above 50% of their capacity during utility peak load hours in the summer months, but at a very low level during winter peak hours.

A process steam generator based on the high temperature magnesium hydride/magnesium heat storage system

As a first pilot project application of the reversible thermochemical high temperature heat storage system magnesium hydride/magnesium a process steam generator has been built and tested. It draws the heat for the generation of superheated steam from a magnesium hydride/magnesium ( MgH 2 Mg ) heat store and is primarily meant for the storage of high grade industrial waste heat which can be made available as superheated process steam during peak load hours. In addition, other areas of application, for instance in high temperature solar engineering, are also opened.

Software design and evaluation of a microcomputer-based automated load forecasting system

Several electric utilities (viz., Virginia Power) in the United States apply demand charge on the basis of coincidental peak hour load (PHL). This is in addition to the usual kWhr charge. The hour during which the utility experiences the highest demand (MW) for the month is called the peak hour and this MW value is referred to here as the PHL. The hourly demand meter readings for the appropriate consumer (e.g., industrial or large commercial customer, wholesale purchaser, etc.) are examined to determine their demand during the peak hour of the month. The monthly demand charge is computed on the basis of the consumer's contribution to this PHL. This monthly capacity charge can run into millions of dollars for industrial and large commercial customers. Thus it is very valuable for the concerned consumer to receive advance information about the time and size of the utility's peak load. This information would enable them to take load control actions such that their demand can be reduced during the hour of peak load. The motivation behind the research reported in this paper is precisely this-to be able to predict the time and size of this PHL. The load forecast software is part of a load forecasting-load management decision support system based on microcomputers. An IBM-RT/PC is used as the central computer around which the load forecasting and load management simulator has been built in our laboratory.