Utility Pricing Research Articles

On the unfairness of actuarial fair annuities

AbstractActuarial fairness pertains to the situation in which the price of an insurance contract is equal to its expected outcome. We show that actuarial fairness leads to “unfairness” in that annuitants with higher survival rates can choose a better payoff in the sense of second-order stochastic dominance than those with lower survival rates. To deal with this issue, we propose equal utility pricing, i.e., we determine prices such that all contracts have the same (nonlinear) utility from the viewpoint of a third party (e.g., a social planner). This approach is of particular relevance with respect to the design of group self-annuitization schemes.

Forecasting Uncertainty Parameters of Virtual Power Plants Using Decision Tree Algorithm

Renewable energy sources (RESs) are increasingly used to meet the world’s growing electrical needs, especially for the economic benefits and environmental problems associated with fossil fuel use. Small-scale renewable energy sources, controllable loads, energy storage devices, and other nonrenewable sources are effectively integrated to form a virtual power plant (VPP). Uncertainty in forecasting renewable energy generation due to the intermittent nature of renewable energy sources is one of the biggest challenges in VPPs. Power generation by RESs changes with the day of the week, season, location, climate, and resource availability. Also, load demand and utility price vary with time and need to be forecasted for proper energy management of VPPs. However, the dispatching and planning of VPPs are significantly impacted by the volatile nature of RESs, load demand, and utility price. Predicting these uncertainties with high accuracy is essential to balance the electrical power generation and the load demand. In this article, a decision tree (DT) algorithm is proposed, to predict the uncertainty parameters, such as the day-ahead power from the RES, load demand, and utility prices of VPPs. The efficiency of the proposed model and the predicted results are compared with other complex models, such as the artificial neural network (ANN) and auto-regressive integrated moving average (ARIMA) algorithms. Root-mean square error (RMSE), mean square error (MSE), coefficient of determination (R 2), and mean absolute error (MAE) are the statistical metrics used to evaluate the accuracy of the prediction. One-year meteorological data of the Chennai zone in India is considered for predicting the uncertainty parameters. IEEE 16-bus and 33-bus test systems are used to validate the forecasting model. It is evident from the results that the proposed DT algorithm can predict the uncertainty parameters more accurately and use lesser time than the ANN and ARIMA algorithms.

Reliability, economic, and environmental analysis of fuel-cell-based hybrid renewable energy networks for residential communities

A fuel cell system has received considerable interest as an effective combined heat and power (CHP) generation system. In particular, a polymer electrolyte membrane fuel cell (PEMFC) has been regarded as suitable for residential buildings as their low-temperature heat generation can be used to meet high heat demand. This study aims to optimize renewable energy networks on the basis of PEMFC, considering both different system mixes in an energy network and different system sizes. This study scrutinizes the overall performance of different hybrid energy networks based on PEMFC in terms of the reliability, economic, and environmental perspectives when both the heat and power generation from PEMFC are used to meet the energy need of residential communities. A hybrid renewable energy network for a residential community with 12 households is modeled and simulated using TRNSYS to evaluate the performance of different hybrid energy network scenarios. Four scenarios are created on the basis of the base case with a gas-based fuel cell (FC) system and heat tank by incrementally adding renewable energy systems—a ground source heat pump (GSHP) system, photovoltaic (PV) system, battery, electrolyzer, and hydrogen storage tank. First, the impacts of individual design variables are quantified using variance-based sensitivity analysis for identification of key design variables. Second, the cost optimization of hybrid energy network is performed under the four scenarios of system mixes. Last, the effects of the key design variables for each scenario are further scrutinized through heat maps. The analysis results of the case study confirms the feasibility of a FC system as part of renewable energy network for residential buildings. Furthermore, Scenario 3 comprising a FC system, GSHP, PV system, and battery is found to be the optimal design in terms of all the three aspects. This result suggests that the whole performance of hybrid energy network can be improved when other renewable systems are included in the network to complement the FC system. In contrast, Scenario 4 using green hydrogen has poorer performance than Scenario 3 using gas as fuel due to high unit prices of hydrogen systems and low efficiency of green hydrogen production. Furthermore, external factors such as utility prices and system unit prices are considered in further analysis and found to have great influence on the optimal design of energy networks.

Techno-economic analysis of a combined heat and power system integrating hybrid photovoltaic-thermal collectors, a Stirling engine and energy storage

This paper presents a comprehensive analysis of the energetic, economic and environmental performance of a micro-combined heat and power (CHP) system that comprises 29.5 m2 of hybrid photovoltaic-thermal (PVT) collectors, a 1-kWe Stirling engine (SE) and energy storage. First, a model for the solar micro-CHP system, which includes a validated transient model for the SE micro-CHP unit, is developed. Parametric analyses are performed throughout a year to evaluate the effects of key component sizes and operating parameters, including collector flow rate, storage tank size, SE micro-CHP flow rate, and battery capacity, on the energetic, economic and environmental performance of the proposed system using real hourly weather data, and thermal and electrical energy demand profiles of a detached house located in London (UK). The optimum component sizes and operating parameters are determined accordingly. The daily and monthly operating characteristics of the system are evaluated, and its annual performance is compared to those of a reference system (gas boiler plus grid electricity), as well as of other alternative solar-CHP systems including a PVT-assisted heat pump system and a standalone PVT system. The results indicate that the installation of such a system can achieve an annual electricity self-sufficiency of 87% and an annual thermal energy demand coverage of 99%, along with annual primary energy savings and carbon emission reduction rate of 35% and 37% relative to the reference system. Over 30 years of operation, the net present value (NPV) of the proposed system is £1990 and the discounted payback period is 28 years. The economics of the proposed system is very sensitive to utility prices, especially the electricity purchase price. Relative to the alternative solar systems, the proposed system offers greater environmental benefits but has a longer payback period. This implies that although the energy saving and emission reduction potential of the proposed system is significant, the initial/capital investment, especially of the SE CHP unit and the PVT collector array, are currently high, so efforts should focus on the cost reduction of these technologies.

A Multidimensional Data Utility Evaluation and Pricing Scheme in the Big Data Market

Big data as a derivative of information technology facilitates the birth of data trading. The technology surrounding the business value of big data has come into focus. However, most of the current research focuses on improving the performance of big data analytics algorithms. Data pricing is still one of the main issues in data trading. Therefore, we aim to tackle the problem of evaluating the utility of data in the big data trading market and the problem of maximizing the profits of the various roles involved in data trading. To this end, we propose a Multidimensional Data Utility Evaluation (MDDUE) method through three data quality dimensions, namely, data size, availability, and completeness. Next, we propose a big data trading market model including data providers, service providers, and service users. An optimal data-pricing scheme based on a three-party Stackelberg game is proposed to maximize the participants’ profits. Finally, a machine learning model is used to verify the rationality and validity of the MDDUE. The results show that MDDUE can evaluate the utility of data more accurately than previous work. The existence and uniqueness of the Nash equilibrium are demonstrated through numerical experiments.

Framework of Transactive Energy Market Strategies for Lucrative Peer-to-Peer Energy Transactions

Leading to the enhancement of smart grid implementation, the peer-to-peer (P2P) energy transaction concept has grown dramatically in recent years allowing the end-users to successfully exchange their excess generation and demand in a more profitable way. This paper presents local energy market (LEM) architecture with various market strategies for P2P energy trading among a set of end-users (consumers and prosumers) in a smart residential locality. In a P2P fashion, prosumers/consumers can export/import the available generation/demand in the LEM at a profit relative to utility prices. A common portal known as the transactive energy market operator (TEMO) is introduced to manage the trading in the LEM. The goal of the TEMO is to develop a transaction agreement among P2P players by establishing a price for each transaction based on the price and trading demand provided by the participants. A few case studies on a location with ten residential P2P participants validate the performance of the proposed TEMO.

Trends in utilization, reimbursement, and price for DOACs and warfarin in the US Medicaid population from 2000 to 2020.

The use of direct oral anticoagulants (DOACs) is widely increasing in the United States (US). Warfarin has been the conventional anticoagulant used in the past few decades, but it has been gradually replaced by DOACs. The objective of the study was to analyze trends in utilization, reimbursement, and price for those anticoagulants in the US Medicaid population. Retrospective data analysis was conducted using the National Summary Files for the Medicaid State Drug Utilization Data. Study drugs included dabigatran, rivaroxaban, apixaban, edoxaban and warfarin. The study assessed secular trends of utilization, reimbursement, and per-prescription price. The data was collected from the first quarter of 2000 through to the second quarter of 2020 restricted for outpatient prescriptions only. During the 21-year study period, a substantial rise in total expenditures on warfarin and DOACs was observed from $144 million in 2000 to $694 million in 2020. Moreover, the utilization of DOACs has increased significantly since the first approval of Xarelto in 2010 from 1079 in 2011 to 1.5 million in 2019. The per-prescription price of DOACs increased from an average of $200 in 2011 to $407 in 2020. Conversely, the total number of prescriptions of Warfarin and branded Coumadin decreased from 2.4 million to 1.4 million and from 3.9 million to less than a million, respectively. The present study demonstrated a change in the trends of US expenditure and utilization for warfarin and DOACs with DOACs representing the majority of market share of both spending per prescription and reimbursement.

Environmental-Techno-Economic analysis of decarbonization strategies for the Indian aluminum industry

The growing demand for aluminum in India faces the challenge of decreasing CO2 emissions in order to be in line with the sustainable development targets. India’s aluminum production uses captive power to provide electricity; captive power is one of the major hotspots in decarbonization. An innovative analysis that combines the aluminum production pathways with carbon capture and storage at both the system level and the country level is conducted. A levelized cost of aluminum (LCOAl) is evaluated from the system level. Results show that LCOAl from primary aluminum ranges from 1682 to 2842 $/tonne depending on different captive power, while the recycling aluminum ranges from 1232 to 1250 $/tonne. Sensitivity analyses show that, affected by the material and utility prices, the LCOAl of primary and recycling aluminum is in the range of 1682–3149 $/tonne, and 460–3112 $/tonne, respectively. Electricity price, scrap price, and recycled ingot price are the most sensitive factors for primary, recycling, and remelt aluminum, respectively. From the environmental perspective on the system level, the primary aluminum pathway is 9 to 32 times more carbon-intensive than the recycling aluminum pathway. At the country level, both primary and secondary aluminum production are considered. An optimized combination of aluminum production pathways and decarbonization strategies are provided with the objective to minimize the total cost. The results show that the CO2 emission with electricity provided from wind- and solar-based captive powers are able to satisfy the beyond 2 °C scenario (B2DS), while natural gas-, diesel-, and coal-based captive powers and electricity from the grid need to remove 2 to 201 million tonnes of CO2 to satisfy the B2DS.

Investigation of process stream systems for targeting energy-capital trade-offs of a heat recovery network

The process industries consume a huge amount of heat energy contributing to environmental impact. Energy recovery is a key instrument of energy-saving that can be implemented via a heat exchanger network. Pinch-based approaches presume analysis of energy targets of industrial processes to find the optimal ΔTmin for heat exchanger network design. Classical Pinch Analysis do not account for the stream splitting in the super targeting procedure while parallel branches are usually needed. The splitters and mixers contribute a lot to the capital cost of the heat exchanger network. Current work proposes the update of super targeting procedure accounting stream splitting and mixing in a Pinch problem. The original algorithm of Composite Curves construction is proposed to analyse the distribution of process streams and stream splitting in subsystems above/below the Pinch before the design of the heat exchanger network. It was then used in a super targeting procedure to precise the capital cost and, as a result, the optimal ΔTmin. The process stream distribution and stream splitting are analysed in a whole range of ΔTmin. Identifying all possible starting points for heat exchanger network design. Two new criteria were proposed to estimate the topological complexity of network pre-design and the specific ratio of stream splitting. The case study analyses the ethylene oxide process and calculation of trade-off between capital and energy costs were performed and optimal ΔTmin = 16 °C. The result was compared with two known approaches, which account for the number of heat exchangers without stream splitting. Total annual costs and optimal ΔTmin was also calculated for different energy prices to show a possible deviation of starting point for heat exchanger network design. The range of optimal ΔTmin from 9 to 54 °C resulted in the range of hot utility prices from 42 to 291 $/kWy, and the emission targets will be from 14,380 to 77,919 tCO2/y. The methodology can be used for the pre-design of the heat exchanger network to better precise the optimal ΔTmin, capital cost targets, and check the optimum changing for different energy prices.

The Utilization, Expenditure, and Price of Angiotensin-Converting Enzyme Inhibitors and Angiotensin Receptor Blockers in the US Medicaid Programs: Trends Over a 31 Year Period

BackgroundAngiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) are used for several indications including hypertension. Our aim is to evaluate the utilization, expenditure, and drug price of ACEIs and ARBs in the US Medicaid population. MethodsA retrospective descriptive trend analysis was conducted using Medicaid State Drug Utilization outpatient pharmacy summary files managed by the Centers for Medicare and Medicaid Services from 1991 to 2021. Study drugs included ACEIs (e.g., captopril) and ARBs (e.g., losartan). Annual reimbursement and utilization were calculated for both classes. The average reimbursement per prescription was calculated as a proxy for drug prices. Market share competition between ACEIs and ARBs was analyzed over time. ResultsACEI and ARB utilization rose by 25% from 1991 to 2021. Brand ACEIs utilization peaked in 2002 with 28 million prescriptions while brand ARBs utilization continued to increase until 2005 with over 23 million prescriptions. However, generic products took the lead and exceeded brand ACEI and ARB utilization in 2006 and 2012 respectively. Medicaid spent over $ 33.7 billion on ACEIs and ARBs over 31-year. Brand ACEIs and ARBs average prices increased sharply to $8,104 and $6,908 respectively in 2021. The total prescription market share for ACEIs was 68% compared to 32% of ARBs over the entire study. ConclusionACEIs and ARBs utilization increased over the last 31 years. Brand utilization switched over to generic resulting in less reimbursement. The average prices of brand ACEIs and ARBs continue to increase even after generics were introduced to the market.

Economic and ecological benefit evaluation of geothermal resource tax policy in China

Geothermal energy is a renewable energy source, and geothermal heating is a livelihood project, so a resource tax can protect resources and regulate prices. Reasonable geothermal energy resource tax collection standards are conducive to high-quality industrial development. This paper takes 15 provinces and cities that clearly levy geothermal resource taxes in China as the research object, constructs an System Dynamics model of geothermal resource tax, and studies the impact of geothermal resource tax on the income of geothermal enterprises and CO2 emission reduction. Then, the forecast simulates the economic and environmental benefits brought about by the use of geothermal resources from 2021 to 2035. The research results show that using geothermal resources for heating can significantly reduce CO2 emissions, and the higher the proportion of geothermal use is, the greater the CO2 emission reduction before 2035. The fifteen provinces and cities that clearly levy geothermal resource taxes will find it difficult to achieve corporate profits at the current residential heating utilization price and geothermal energy resource tax rate; that is, to achieve a balance of income and expenditures for geothermal enterprises without financial subsidies, the price of geothermal heating will be significantly higher than the heating price of traditional central heating and the price of electric self-heating.

The role of individual service and team-based service price in the online environment: A view from the price difference.

Different from the traditional medical market, the online medical market allows physicians considerable discretion in setting prices of their services, which is beginning to be paid close attention to. Physicians face a challenge with the introduction of various service styles. Guided by transaction utility theory and price fairness, this study aims to investigate the influence of pricing strategy on service demands from the price difference perspective by focusing on two typical service models: individual service and team-based service. Moreover, team characteristics (response speed and team size) are also considered. The data collection was done in March 2018 and repeated in May 2018, and physicians who provide both individual service and team-based services are included in our study. Finally, a dataset consisting of 1,100 teams with 1,100 physician leaders from 14 departments such as obstetrics and gynecology department were collected from an online medical platform in China. Empirical results support most of our hypotheses. A negative influence of team-based price was observed. As a substitute service, a higher individual service price will make patients turn to team-based service. Moreover, individual service prices negatively moderated the relationship between team-based service prices and demands. By calculating the price difference between the individual service price and the team-based service price, we found a negative role of the price difference affecting patient purchase decisions. Although we did not find a significant effect of team size, a quick response can attract more patients. Price fairness provides a proper framework for understanding pricing strategy in individual and team-based service in an online environment. Understanding the effects of prices from a price difference perspective has both theoretical and practical contributions. Specifically, this study contributes to knowledge on price fairness, online medical platforms, and virtual teams, and provides management suggestions.

Process Development for Methyl Isobutyl Ketone Production Using the Low-Pressure One-Step Gas-Phase Selective Hydrogenation of Acetone

Methyl isobutyl ketone (MIBK) is a highly valuable product in the chemical industry. It is widely used as an extracting agent for heavy metals, antibiotics, and lubricating oils. Generally, MIBK can be produced by three-step and one-step liquid-phase methods. These methods are expensive and energy-demanding due to the high pressure and low conversion of acetone. A novel nano-Pd/nano-ZnCr2O4 catalyst was developed to produce MIBK with high conversion and selectivity of 77.3% and 72.1%, respectively, at 350 °C and ambient pressure, eliminating the need for high pressure in conventional MIBK processes. This study is the first that proposes a newly developed process of methyl isobutyl ketone (MIBK) production using the low-pressure one-step gas-phase selective hydrogenation of acetone. In this work, a novel process flow diagram has been developed for the production of MIBK using the developed nano-catalyst. The process was heat integrated, resulting in a 26% and a 19.5% reduction in the heating and cooling utilities, respectively, leading to a 12.6% reduction in the total energy demand. An economic analysis was performed to determine the economic feasibility of the developed process, which shows that the process is highly profitable, in which it reduced both the capital and operating costs of MIBK synthesis and showed a return on investment (ROI) of 29.6% with a payback period of 2.2 years. It was found that the ROI could be increased by 18% when the reactor temperature is increased to 350 °C. In addition, the economic sensitivity analysis showed that the process is highly sensitive to product prices and least sensitive to utility prices, which is due to the versatility of the process that requires only a low amount of energy.

Price Regulation Under Sanctions: International Experience

The object of the article is price shocks in Russia resulting from international sanctions imposed in connection with military action in Ukraine. The aim of the paper is to examine the most useful international experience of administrative price regulation for vital commodities. In particular, the study describes price control in India, China, Kyrgyzstan, Venezuela, Iran, Bolivia, Saudi Arabia, as well as in Russia itself. The synthesis of regulatory practices forms the basis of a generalised scheme, taking into account the stages, scale, timing and methods of government intervention. Furthermore, the need to combine static and dynamic price regulation for different product groups is stressed. Among the countries considered, the most interesting and original experience is that of India's regulation of corporate stocks (e.g. retail chains) to give rhythmic sales and the practice of marking the Maximum Retail Price (MRP) for certain types of goods. The authors argue for the possibility of adjusting the utility price index to account for sharp spikes in inflation in the initial period of sanctions, and for reducing the operational period for monitoring prices for decision-making from 60 to 30 days.

Local public right of way for surface and subsurface resource integration

ABSTRACT A utilidor is a ‘system of systems’ infrastructural solution to the ‘subsurface spaghetti’ problem resulting from direct burial of utility transmission infrastructure beneath the public right of way (PROW). The transition from direct burial to utilidors in older, dense American cities has generally not occurred, despite the potential to increase system performance in a long-term, financially and environmentally sustainable manner, because it would require reform of local planning practices and of utility pricing to support financing within a complex regulatory system. Utilidor adoption in New York City (NYC) would be a significant local infrastructure transition, amplifying the need for locality-based research, that would occur while each utility sector undergoes its own infrastructure transitions, thereby increasing the level of regulatory complexity. This paper applies transitions analysis, recursive collective action theory, and capacity to act analysis to NYC’s experience with its PROW subsurface spaghetti problem and utilidor implementation to demonstrate a place-based methodology that identifies specific sources of resistance to innovative subsurface design and feasible pathways for resolving them. This methodology would be transferable for application to other American cities or classes of American cities to supplement the limits of generalised subsurface and subsurface resource integration research for practitioner application.

Optimal Sharing Electricity and Thermal Energy Integration for an Energy Community in the Perspective of 100% RES Scenario

This paper presents a study on the optimal district integration of a distributed generation (DG) system for an energy community (EC) and the implementation of sharing electricity (SE) between users. In recent years, the scientific community has frequently discussed potential pathways to achieve a 100% renewable energy source (RES) scenario, mainly through increasing electrification in all sectors. However, cooling-, heat-, and power-related technologies are expected to play a crucial role in the transition to a 100% RES scenario. For this reason, a research gap has been identified when it comes to an optimal SE solution and its effects on the optimal district heating and cooling network (DHCN) allowing both electrical and thermal integration among users. The considered system includes several components for each EC user, with a central unit and a DHCN connecting them all. Moreover, the users inside the EC can exchange electricity with each other through the existing electric grid. Furthermore, the EC considers cooling storage as well as heat storage systems. This paper applies the Mixed Integer Linear Programming (MILP) methodology for the single-objective optimization of an EC, in Northeast Italy, considering the total annual cost for owning, operating, and maintaining the entire system as the economic objective function. After the optimization, the total annual CO2 emissions were calculated to evaluate the environmental effects of the different solutions. The energy system is optimized in different scenarios, considering the usage of renewable resources and different prices for the purchase of electricity and natural gas, as well as different prices for selling electricity. Results showed that, without changing utility prices, the implementation of SE allowed for a reduction of 85% in the total electricity bought from the grid by the EC. Moreover, the total annual EC costs and CO2 emissions were reduced by 80 k€ and 280 t, respectively.

A stochastic optimization framework for integrated scheduling and control under demand uncertainty

Increased globalization and energy market deregulation are requiring process industries to respond more rapidly to fluctuations in demand levels, and utility and raw material prices, in order to remain competitive. In this study, a two-stage stochastic approach is proposed to account for demand uncertainty in a closed-loop dynamic real-time optimization (CL-DRTO) formulation that includes scheduling decisions. The CL-DRTO problem utilizes a prediction of the closed-loop response of the plant under the action of constrained MPC. The CL-DRTO system is executed in a rolling horizon fashion to compute economically optimal operation that is communicated to the plant through set-point trajectories assigned to the plant MPC. Nonlinear plant models are approximated using linear and piecewise affine (PWA) approximations, allowing the integrated CL-DRTO problem to be formulated as a mixed-integer linear program (MILP). Case studies demonstrate significantly higher expected profit using the proposed formulation than with a deterministic formulation utilizing the expected demand.

Application of Magnetic Resonance DTI Technique in Evaluating the Effect of Postoperative Exercise Rehabilitation

Magnetic resonance diffusion tensor imaging (DTI) is a new kind of magnetic resonance imaging technology. Its imaging principle is to distinguish different pathological tissues according to the movement of water molecules, which is higher than regular magnetic resonance diffusion-weighted imaging. Magnetic resonance diffusion tensor imaging has exact utility price in medical analysis and sickness evaluation. However, there are few researches on the utility of diffusion tensor imaging in the rehabilitation comparison of patients. This paper explores the utility of magnetic resonance DTI science in evaluating the impact of postoperative patients' exercising rehabilitation. Taking stroke patients as an example, through giving patients rehabilitation training method, using magnetic resonance DTI technology, the motor function rehabilitation of patients was evaluated, and FA changes of the affected side and healthy side and Fugl–Meyer score of two groups of patients before and after rehabilitation were observed. The software outcomes exhibit that, in the contrast of rehabilitation therapy impact of motor feature in sufferers with cerebral infarction, the use of magnetic resonance DTI technological know-how gives a foundation for clinicians to deeply apprehend the CST involvement of patients, which helps to scientifically evaluate the effect and quality of limb motor rehabilitation training of patients and provides a basis for disease treatment.

Design parametric analysis of low-energy residential buildings on the way to a defined cost-optimal capacity point

One of the most energy-saving design aspects is the reliability of the envelopes, which helps to monitor the temperature of the building and thus reduces dependence on mechanical heating, ventilation, and air conditioning systems for heating or cooling the building. This study designs home retrofits and new construction of single-family buildings by assessing parametric sweeps, building design, and cost-based energy savings building analysis. The goal of this research is to determine the most cost-effective paths for a single-family building. For building design, we chose nine capacity points with the lowest cost. BEopt offers comprehensive building-based assessments, including scale, occupancy, design, location, vintage, and utility prices. Equipment and discrete envelope options that reflect realistic building practices and materials are evaluated. The impact of carbon emission reductions is calculated on an annual basis. The proposed building's energy savings are calculated concerning a B10 benchmark. The parametric analysis results were compared to the B10 Benchmark, and the proposed building energy consumption of Point 4 (12.4%), Point 6 (13%), Point 8 (7.4%), and Point 9 (8.2%) was lower than the B10 Benchmark. Point 6 is our proposed design's most cost-effective capacity point. Our proposed method allows us to analyze residential building structures and define cost-optimal performance packages at different energy consumption rates in all single-family buildings. Users can specify options to be considered in the optimization by selecting predefined options from various categories.

ENERGY SAVING AS AN INDICATOR OF ENERGY EFFICIENT PRODUCTION

The increase in utility prices in Russia occurs every year. In modern society, as well as in the production sector, saving are the main indicator of successful development. (Research purpose) The research purpose is analyzing an automated system for the technical accounting of energy resources, which is designed to help the enterprise redistribute the load from the main time, when the price is high, to the night, when the price is lower. (Materials and methods) Manufacturing enterprises are trying to reduce their energy costs, monitor all saving innovations and use them in their activities. (Results and discussion) The companies try to use: strict requirements for control over the lighting in the entire building, automatic shutdown devices, constantly updates the equipment. Enterprises mainly use the same technologies to save electricity. Some enterprises use direct savings of electric energy, which leads to a reduction in costs due to equipment; installation of such equipment can only be carried out with available energy consumption data. In order to get these data, a special automated information and measurement system is used. (Conclusions) The choice of each electric motor should be individual, only if the specified parameters are met and the goals for saving electricity are realized, an entrepreneur can count on leading positions in a competitive market. The automated information and measurement system opens up a number of advantages for saving electrical energy.