Annual Electricity Cost Research Articles

Operational optimization of a grid-connected factory with onsite photovoltaic and battery storage systems

Driven by fast advancements in wind and photovoltaic (PV) technologies, onsite renewable electricity generation is becoming attractive to manufacturers since they are able to reduce electricity purchases from the grid and may lower their electricity costs. This paper proposes a methodology to minimize the electricity cost of a grid-connected factory that also has onsite solar power generation and battery storage. Purchases from the grid are subject to time-of-use electricity rate schedules. The problem is formulated asa mixed-integer programming problem and GAMS is used to find the optimal manufacturing and onsite energy flow schedules that have the minimal electricity cost. A case study with one hybrid flow shop, onsite PV power generation, and a battery was used to test the proposed method. Testing results showed that the factory’s electricity cost can be reduced by 54.0% under summer TOU rate on a typical day while a 0.7% electricity cost reduction can be achieved for a representative day under a winter TOU rate. An annual electricity cost savings of 28.1% can be obtained with the optimal schedules. In addition, a parametric study incorporating the optimal schedules was performed to understand the economic performances associated with different PV capacity and battery bank size for the factory.

Economics of household wind turbine grid-tied systems for five wind resource levels and alternative grid pricing rates

Households in the USA state of Oklahoma serviced by investor owned electric utilities that have smart meters may select to be charged based on either a traditional meter rate schedule, a smart meter schedule, or they may install a household grid-tied wind turbine and be subject to a different rate schedule. The objective of the research was to determine the economic consequences of installing microgeneration grid-tied wind turbine systems (6 kW; 10 kW) given alternative pricing structures for households at five unique locations with different wind resources. Twenty years of hourly wind speed data, and hourly electricity use data for representative households, were obtained for each location. The annual household electricity cost among the five locations ranged from $894 to $1199 for the smart meter rates and $870–$1191 for the traditional meter rates. The estimated annual cost of $5389 for the least costly household grid-tied 6 kW wind turbine system, is five times greater than the annual cost of purchasing from the grid. If external consequences of electricity generation and distribution are ignored, given current and proposed rate structures and prices, household wind turbine electricity generation systems are not economically competitive in the region.

DESIGN SOLUTIONS ON THE LOW TEMPERATURE CORROSION PROBLEM FOR HEATING SURFACES OF HEAT EXCHANGERS AT GAS TURBINE CHP

The article is devoted to the problem of low temperature corrosion of heating surfaces occurring in the non-heating season in heat exchangers of gas turbine cogeneration power plant when working on 150/70 heating system temperature chart. An overview is presented of the existing methods of eliminating such corrosion, including the analysis of their drawbacks. A new approach to ensuring the required temperature level of water in the heat exchangers is proposed. Based on the proposed approach, design solutions are developed and proposed aimed at eliminating the problem of low temperature corrosion of the heating surfaces during the non-heating season. The developed solutions are based on the arrangement of loop pre-heating of the network water where the reverse network water is heated to the minimum allowable temperature at the inlet to the gas-water heat exchanger (GWHE). Two options of thermal circuits with a closed circuit coolant circulation are proposed. The circuits differ in the method of heating the heating medium flowing in WTO: the first one operates by burning fuels in boilers, and the second one by using the heat of the exhaust gas entering the dedicated heating surface of GWHE. The advantage of the first option is the ability to disable the loop pre-heating at low outdoor temperatures when the temperature of the water at the inlet to GWHE exceeds the allowed minimum which reduces the annual cost of electricity for own needs. The advantage of the second option of the thermal circuit is a reduction in energy costs of the circulation pump operation by reducing the flow of coolant circulating in a closed circuit. The calculations of the proposed thermal schemes allowed to conclude that the proposed methods of closed loop pre-heating of the network water provide acceptable water temperature in GWHE with consideration of CHP operation modes throughout the year and help to prevent low-temperature corrosion of heat exchangers heating surfaces.

Consumer valuations of energy efficiency investments: The case of Vietnam's Air Conditioner market

Research on the energy-efficiency gap in developing countries remains very scant. In this study, we apply the hedonic price model to investigate how Vietnamese consumers value the energy efficiency of air conditioners (ACs). We assume that the energy efficiency of ACs in the Vietnamese market improves to that of ACs in the Japanese market. Then, we calculate the payback period by dividing the capital cost to improve the energy efficiency by the annual electricity cost saving. We show that the initial investment cost can be recovered in a short time. In addition, we calculate the implicit discount rate to show how Vietnamese consumers value energy efficiency investment. We find that the implicit discount rate in Vietnam's AC market is much higher than the rates found in studies on developed countries. Hence, consumers in developing countries place much lower value on energy efficiency investment than consumers in developing countries, even though purchasing energy-efficient appliances offers opportunities to save substantial amounts.

Environmental Assessment of Smart Grid Station Project Centered on Pilot Project of Korea Electric Power Corporation Building

Increased evidences reveal that the global climate change adversely affect on the environment. Smart grid system is one of the ways to reduce greenhouse gas emissions in the electricity generation sector. Since 2013, Korea Electric Power Corporation (KEPCO) has installed smart grid station in KEPCO office buildings. The goal of this paper is two folds. One is to quantify the reduction in greenhouse gas emissions through smart grid stations installed in KEPCO office buildings as a part of pilot project. Among components of smart grid stations, this research focused on the photovoltaic power system (PV) and energy storage system (ESS). The other is to estimate the reduction in greenhouse gas emissions when PV is applied on individual houses. Results show that greenhouse gas emissions reduce 5.8∼11.3% of the emissions generated through the electricity usage after PV is applied in KEPCO office buildings. The greenhouse gas emissions reduction from ESS is not apparent. When PV of 200∼500 W is installed in individual houses, annual greenhouse gas emission reduction in 2016 is expected to be approximately 2.2∼5.4 million tCO 2 -eq, equivalent to 6∼15% of greenhouse gas emissions through the electricity usage in the house hold sector. The saving of annual electricity cost in the individual house through PV of 200 W and 500 W is expected to be 47∼179 thous and KRW and 123∼451 thousand KRW, respectively. Results analyzed in this study show the environmental effect of the smart grid station. In addition, the results can be further used as guidance in implementing similar projects.

A novel PSO (Particle Swarm Optimization)-based approach for optimal schedule of refrigerators using experimental models

Refrigerators have considerable share of residential consumption. They can be, however, flexible loads because their operating time and consumption patterns can be changed to some extent. Accordingly, they can be selected as a target for the study of Demand Side Management plans. In this paper, two experimental models for a refrigerator are derived. In obtaining the first model, following assumptions are made: the ambient temperature of refrigerator is assumed to be constant and the refrigerator door is remained closed. However, in the second model the variation of ambient temperature and door-opening effects are considered according to some general patterns. Further, two strategies are proposed to reduce the annual electricity cost and electric power consumption at peak-load times. These strategies together with the aforementioned models form an optimization problem which is, then, solved by Particle Swarm Optimization algorithm. Simulation results indicate a reduction of more than 28.61% in the annual cost. Also, the annual electricity consumption has decreased more than 20.46% and load shifting from the peak periods has achieved about 40%. In addition, these approaches are implemented in laboratory and their performance is confirmed by experimental results.

Does an Embedded Wind Turbine Reduce a Company\u2019s Electricity Bill? Case Study of a 300\xa0kW Wind Turbine in Ireland

In recent years, a growing number of small-to-medium-enterprises are embracing wind turbine projects not only as part of their cost reduction strategy but also to actively play their part in the global fight against climate change. However, it would appear there are currently limited empirical studies carried out in this emerging industry. This case study analyses the cost effectiveness of one such wind turbine initiative by a company in the Republic of Ireland, who invested in a 300 kW embedded wind turbine project at the end of 2013. The research methodology which is primarily a case study analysis included comparing historical electricity utility bills which allowed the 2013 quantity of electrical energy units imported, i.e. the year before the turbine was installed to be compared with the 2014 value, i.e. the year after the turbine was installed. Numerous site visits were undertaken over a four-year period, during which electric meter readings were recorded and stored. The findings of this piece of research indicate that the installation of the embedded wind turbine had minimal positive effect on the annual electricity costs for the company. Indeed the turbine appears to have significant negative effects such as a need for an increased maximum import capacity and also it appeared to contribute to a deteriorating utility power factor. While the aesthetic nature of the on-site turbine seemed to create a positive image of the company, it would appear that caution should be exercised when business owners select alternative energy providers who claim to be experts in the energy field but may have limited knowledge in this area of wind energy, which as of yet has minimal robust research into all aspects of its benefits/attributes.

Annual energy analysis of concrete containing phase change materials for building envelopes

This paper examines the annual energy and cost savings potential of adding microencapsulated phase change material to the exterior concrete walls of an average-sized single family home in California climate zones 3 (San Francisco, CA) and 9 (Los Angeles, CA). The annual energy and cost savings were larger for South- and West-facing walls than for other walls. They were also the largest when the phase change temperature was near the desired indoor temperature. The addition of microencapsulated phase change material to the building walls reduced the cooling load in summer substantially more than the heating load in winter. This was attributed to the cold winter temperatures resulting in nearly unidirectional heat flux on many days. The annual cooling load reduction in an average-sized single family home in San Francisco and in Los Angeles ranged from 85% to 100% and from 53% to 82%, respectively, for phase change material volume fraction ranging from 0.1 to 0.3. The corresponding annual electricity cost savings ranged from $36 to $42 in San Francisco and from $94 to $143 in Los Angeles. From an energy standpoint, the best climate for using building materials containing uniformly distributed microencapsulated phase change material would have outdoor temperature oscillations centered around the desired indoor temperature for the entire year.

Reciprocating and Screw Compressor semi-empirical models for establishing minimum energy performance standards

The efficiency bar for a Minimum Equipment Performance Standard (MEPS) generally aims to minimize energy consumption and life cycle cost of a given chiller type and size category serving a typical load profile. Compressor type has a significant chiller performance impact. Performance of screw and reciprocating compressors is expressed in terms of pressure ratio and speed for a given refrigerant and suction density. Isentropic efficiency for a screw compressor is strongly affected by under- and over-compression (UOC) processes. The theoretical simple physical UOC model involves a compressor-specific (but sometimes unknown) volume index parameter and the real gas properties of the refrigerant used. Isentropic efficiency is estimated by the UOC model and a bi-cubic, used to account for flow, friction and electrical losses. The unknown volume index, a smoothing parameter (to flatten the UOC model peak) and bi-cubic coefficients are identified by curve fitting to minimize an appropriate residual norm. Chiller performance maps are produced for each compressor type by selecting optimized sub-cooling and condenser fan speed options in a generic component-based chiller model. SEER is the sum of hourly load (from a typical building in the climate of interest) and specific power for the same hourly conditions. An empirical UAE cooling load model, scalable to any equipment capacity, is used to establish proposed UAE MEPS. Annual electricity use and cost, determined from SEER and annual cooling load, and chiller component cost data are used to find optimal chiller designs and perform life-cycle cost comparison between screw and reciprocating compressor-based chillers. This process may be applied to any climate/load model in order to establish optimized MEPS for any country and/or region.

Optimal annual operation of the dry cooling system of a concentrated solar energy plant in the south of Spain

This work presents the optimization of the operation of a concentrated solar power plant with dry cooling over a year, evaluating the molten salts storage, the power block and the air cooling system as a function of the climate and atmospheric conditions. We locate the plant in the south of Europe, Almería (Spain), due to the high solar irradiation and for comparison purposes with a wet cooling based facility. The optimization of the system is formulated as a multiperiod MINLP (mixed integer non-linear programming problem) that is solved for the optimal production of electricity over a year defining the main operating variables of the thermal cycles and the cooling system. The power produced ranges from 9.5 MW in winter to 25 MW in summer, where 5% of this power is consumed by the air cooling system. The annual production cost of electricity is 0.16 €/kWh and the investment required is 265 M€, both slightly higher than when wet cooling is used, but with negligible water consumption. For the selected location, the wet based technology generates slightly less CO2 than the air cooled facility.

Assessing the impact of changes in the electricity price structure on dairy farm energy costs

This study aims to provide information on the changes in electricity consumption and costs on dairy farms, through the simulation of various electricity tariffs that may exist in the future and how these tariffs interact with changes in farm management (i.e. shifting the milking operation to an earlier or later time of the day). A previously developed model capable of simulating electricity consumption and costs on dairy farms (MECD) was used to simulate five different electricity tariffs (Flat, Day&Night, Time of Use Tariff 1 (TOU1), TOU2 and Real Time Pricing (RTP)) on three representative Irish dairy farms: a small farm (SF), a medium farm (MF) and a large farm (LF). The Flat tariff consisted of one electricity price for all time periods, the Day&Night tariff consisted of two electricity prices, a high rate from 09:00 to 00:00h and a low rate thereafter. The TOU tariff structure was similar to that of the Day&Night tariff except that a peak price band was introduced between 17:00 and 19:00h. The RTP tariff varied dynamically according to the electricity demand on the national grid. The model used in these simulations was a mechanistic mathematical representation of the electricity consumption that simulated farm equipment under the following headings; milk cooling system, water heating system, milking machine system, lighting systems, water pump systems and the winter housing facilities. The effect of milking start time was simulated to determine the effect on electricity consumption and costs at farm level. The earliest AM milking start time and the latest PM milking start time resulted in the lowest energy consumption. The difference between the lowest and highest electricity consumption within a farm was 7% for SF, 5% for MF and 5% for LF. This difference was accounted for by the variation in the milk cooling system coefficient of performance. The greatest scope to reduce total annual electricity costs by adjusting milking start times was on TOU2 (39%, 34% and 33% of total annual electricity costs on the SF, MF and LF) and the least scope for reductions using this method was on the Flat tariff (7%, 5% and 7% of total annual electricity costs). The potential for reduction of annual electricity consumption and related costs per litre of milk produced by adjusting milking times was higher for the LF than the SF or MF across all electricity tariffs. It is anticipated that these results and the use of the MECD will help support the decision-making process at farm level around increasing energy efficiency and electricity cost forecasts in future electricity pricing tariff structures.

Modeling, simulation and feasibility analysis of residential BIPV/T+ASHP system in cold climate—Canada

A TRNSYS model was developed to integrate photovoltaic/thermal collector (PV/T) in a roof and coupled with Air Source Heat Pump (ASHP) in an Archetype Sustainable House (ASH). The heat pump uses the warm air generated in the Building Integrated Photovoltaic-Thermal Collector (BIPV/T) as the source for heat production. The coupling of BIPV/T and ASHP enables a highly efficient heating system in winter conditions. The developed TRNSYS model was simulated for different regions to predict the seasonal performance of the heat pump. The results from the simulation were used to estimate the saving in energy and cost as well as to predict the electricity related greenhouse gas (GHG) emission reduction potential from the PV panels. The results showed that annual GHG emission due to electricity demand by the ASHP was reduced by 225kg CO2 for ASHP+PV/T. The annual electricity cost credit from PV production based on Time-of-Use (TOU) and the reduction in electricity cost of the heat pump when connected with PV/T systems was calculated and compared with the cost of working the heat pump alone. The results showed that there was a saving of $500 in annual electricity bills and GHG emission credit of 1734.7kg CO2 from renewable electricity generation.

Introduction of electrical batteries in the operation of LowEx buildings

It is possible to reshape the building's post-control electricity consumption by introducing an electrical storage element. This paper presents a parametric stochastic optimization model, that combined with a heuristic decision-making method, allows to determine the most suitable electrical battery capacity to be installed in a Low Exergy (LowEx) multi-family house located in the temperate climate zone of Middle Europe.In contrast to most common battery models, this optimization is not formulated in terms of the battery payback period. This model does not explicitly consider the battery cost, but aims instead at a twofold objective, namely to minimize the normalized annual electricity cost and simultaneously peak-shave the 15-minute average electrical power.The sensitivity of the model to uncertainties is accounted for by introducing stochastic elements in several model components. The natural variability in the solar electricity production is modeled by using a historical 12-year-data period, and the estimated electricity consumption by occupants is formulated as a probability distribution. Multiple scenarios are hypothesized in order to evaluate the model performance under different conditions. This model assumes a market-based dynamic electricity-pricing scheme. The effect of the dynamic electricity prices is examined by evaluating the model's behavior when subjected to spot market prices from three different geographically close locations.

Agent-based model for electricity consumption and storage to evaluate economic viability of tariff arbitrage for residential sector demand response

Demand response (DR) is one of many approaches to address temporal mismatches in demand and supply of grid electricity. More common in the commercial sector, DR usually refers to reducing consumption at certain hours or seasons, thus reducing peak demand from the grid. In the residential sector, where sophisticated appliance-level controls such as automatic dimming of lights or on-demand lowering of air conditioning are less common, building-based electricity storage to shift grid consumption from peak to off-peak times could provide DR without requiring consumers to operate their appliances on shifted or reduced schedules: Storage would be dispatched to appliances as needed while still shaving peaks on the grid. Technologically, storage and two-way-inverters are readily available to enable such residential DR. Economically, however, the situation is less clear. Specifically, are time-varying electricity tariffs available such that electricity cost reduction via arbitrage could offset manufacturing, financing, and installation costs of the required storage? To address this question we (i) devise an agent-based appliance-level stochastic model to simulate the electricity demand of an average U.S. household; (ii) loadshift the demand via simple dispatch strategies; and (iii) determine potential profits to the building owner, i.e. reduced electricity cost of the modified demand with realistic tariffs (Con Edison, NY) minus storage cost. We determine the economic viability for a range of traditional and advanced storage technologies as well as their optimum storage capacities to maximize profits. We find that (i) profits can range from <1% to 48% of annual electricity costs of a typical household; and (ii) optimum capacities, while approximately equal to households’ kWh consumption during peak hours, is affected by stochastic variations in daily and seasonal consumption. Future improvements to storage technology, arbitrage strategies, and tariffs are discussed. Details of the storage technologies, agent-based model, testing, and benchmarking are supplied as Supplementary Data.

Electrical efficiency study of alternating current and direct current power topologies in office building with integrated photovoltaics in Matlab-Simulink environment

Electrical efficiency and the energy balance of an office building (Espoo, Finland: 60.11°N, 24.49°E) with an integrated photovoltaic system were studied using a Matlab-Simulink model. The model consisted of measured on-site photovoltaic data, measured luminaire data, and consumption data of computers and ventilation system that was obtained from online sources. Similar model was studied with four different voltages: 12 V DC, 24 V DC, 48 V DC, and 230 V AC, and the system components were selected accordingly. All components selected for the simulations were commercially available. Light-emitting diode (LED)- and fluorescent-lamp lighting were used in the simulation, and they were compared to each other. Based on the simulations, the annual energy balance of each simulated option was calculated, and the annual grid electricity costs were estimated. The results were calculated for similar systems with and without battery backup system. The energy balance of the building was studied in different months, as the seasonal differences in the available solar energy vary considerably in the location of the simulated building. The total energy consumption of the building was lowest when 230 V AC was used as the system voltage. This was mainly due to the fact that computer power supplies designed for smaller DC voltages were considerably less energy efficient, than those designed for 230 V AC. When only the energy efficiency of the lighting was considered, 48 V DC provided best results with LED lighting and 24 V DC with the fluorescent lamp lighting. The LED lighting was more energy efficient than the fluorescent lamp lighting in all studied voltages. 12 V DC system required thicker wires than the other options, and even then, the wire losses were more prominent. Battery backup system reduced the amount of bought and sold grid energy in all tested situations. Whether this is economically sensible depends on the price of the sold and bought grid energy. In this study, the initial assumption was that the price was the same regardless of the direction of energy flow, and in that case, the battery backup provided no savings, in terms of direct energy costs. The seasonal differences in the grid energy requirement were considerable. In July, the building was self sufficient in electricity production on a daily level, in April, about 80% of the electricity was produced locally, and in December, only 2%–5% of the needed electricity was produced locally.

Optimization of modified ABA2 process using linearized ASM2 for saving aeration energy

Abstract In the recent years, increasingly strict demands on the effluent quality of wastewater treatment plants (WWTPs) and concern for energy consumption have led to the growing interest in the development of an optimization and control scheme for treatment processes. In general, operation of WWTPs can be controlled either by real-time monitoring of major constituents or through model prediction. In this research, a simplified ASM2 model was applied to describe the dynamics of NH 4 + , NO 3 − and PO 4 3− in a modified anaerobic, buffer, intermittently aerated, and oxic pilot-scale plant (treatment capacity = 50 m 3 d −1 ). Using this model, the system performance was optimized by adjusting aeration and non-aeration time to control effluent ammonia level to preset values and at the same time to minimize energy consumption for aeration. As a result of the operation, the system could achieve 89%, 73%, and 82% removal efficiencies for COD, TN, and TP, respectively. The average f a (aeration cycle time/total cycle time) values were 0.46 for optimization as compared to 0.57 for normal operation. The standard errors between model predictions and measured effluent NH 4 + –N were 0.1 mg L −1 for the optimization scheme. With an application of optimizer, it can maintain good effluent water quality while achieving energy saving. For our pilot scale plant, it was calculated that annual electricity cost of about 600 USD could be saved if the plant would be continuously operated by the optimization scheme.

Optimal deployment of thermal energy storage under diverse economic and climate conditions

This paper presents an investigation of the economic benefit of thermal energy storage (TES) for cooling, across a range of economic and climate conditions. Chilled water TES systems are simulated for a large office building in four distinct locations, Miami in the U.S.; Lisbon, Portugal; Shanghai, China; and Mumbai, India. Optimal system size and operating schedules are determined using the optimization model DER-CAM, such that total cost, including electricity and amortized capital costs are minimized. The economic impacts of each optimized TES system is then compared to systems sized using a simple heuristic method, which bases system size as fraction (50% and 100%) of total daily on-peak summer cooling loads.Results indicate that TES systems of all sizes can be effective in reducing annual electricity costs (5–15%) and peak electricity consumption (13–33%). The investigation also identifies a number of criteria which drive TES investment, including low capital costs, electricity tariffs with high power demand charges and prolonged cooling seasons. In locations where these drivers clearly exist, the heuristically sized systems capture much of the value of optimally sized systems; between 60% and 100% in terms of net present value. However, in instances where these drivers are less pronounced, the heuristic tends to oversize systems, and optimization becomes crucial to ensure economically beneficial deployment of TES, increasing the net present value of heuristically sized systems by as much as 10 times in some instances.

Optimal Sizing of BESS for Customer Demand Management

AbstractThe paper proposes an optimal sizing method of a customer's Battery Energy Storage System(BESS) which aims at managing the electricity demand of the customer to minimize electricity cost under the time of use(TOU) pricing. Peak load limit of the customer and charging and discharging schedules of the BESS are optimized on annual basis to minimize annual electricity cost, which consists of peak load related basic cost and actual usage cost(energy cost). The optimal scheduling is used to assess the maximum cost savings for all sets of candidate capacities of BESS. An optimal size of BESS is determined from the cost reduction rate(hereinafter reduction rate) curves via capacity of BESS. Case study uses real data from industry customer and shows how the proposed method can be employed to optimally design the size of BESS for customer demand management.

Optimal year-round operation of a concentrated solar energy plant in the south of Europe

We present the year-round optimization of the operation of a concentrated solar power facility evaluating the molten salts storage, the power block and cooling. We locate the plant in the south of Europe, Almería (Spain), where high solar radiation is available. The operation of the plant is a function of the solar incidence as well as the climate and atmospheric conditions. The optimization of the system is formulated as a multiperiod Non-linear Programming problem (NLP) that is solved for the optimal production of electricity over a year defining the main operating variables of the thermal and cooling cycles. For a maximum of 25 MW in summer and a minimum of 9.5 MW in winter the annual production cost of electricity is 0.15 €/kWh consuming an average of 2.1 Lwater/kWh. The investment for the plant is 260 M€. Scale-up studies reveal that the production cost can decrease by half while the investment per unit of power should become competitive with current coal based power plants if solar and coal facilities present similar production capacities.

수용가 수요관리용 전지전력저장시스템의 최적용량 산정방법

The paper proposes an optimal sizing method of a customer"s battery energy storage system (BESS) which aims at managing the electricity demand of the customer to minimize electricity cost under the time of use(TOU) pricing. Peak load limit of the customer and charging and discharging schedules of the BESS are optimized on annual basis to minimize annual electricity cost, which consists of peak load related basic cost and actual usage cost. The optimal scheduling is used to assess the maximum cost savings for all sets of candidate capacities of BESS. An optimal size of BESS is determined from the cost saving curves via capacity of BESS. Case study uses real data from an apartment-type factory customer and shows how the proposed method can be employed to optimally design the size of BESS for customer demand management.