Impact of Building Location on its Energy Demand Pokorska-Silva, Iwona; Nowoświat, Artur; Gać, Weronika
IOP conference series. Materials Science and Engineering,
11/2021, Volume:
1203, Issue:
3
Journal Article
Peer reviewed
Open access
Abstract
The paper presents the analyses involving energy demand of a single-family building located in various climatic zones. When designing buildings, special attention is paid to material and ...technological solutions, but often the climatic zone in which the building is to be located is not taken into account. Therefore, the article considers the location of building in five climatic zones in Poland and it investigates the impact of the location on its energy demand. It turned out that the location of the building in zone V, i.e. in the north-east of the country, determines the highest energy demand for heating compared to the rest of the country. The work demonstrates the impact of a climatic zone in which the building is located on its energy demand.
According to the feed-in tariff for encouraging local consumption of photovoltaic (PV) energy, the energy sharing among neighboring PV prosumers in the microgrid could be more economical than the ...independent operation of prosumers. For microgrids of peer-to-peer PV prosumers, an energy-sharing model with price-based demand response is proposed. First, a dynamical internal pricing model is formulated for the operation of energy-sharing zone, which is defined based on the supply and demand ratio (SDR) of shared PV energy. Moreover, considering the energy consumption flexibility of prosumers, an equivalent cost model is designed in terms of economic cost and users' willingness. As the internal prices are coupled with SDR in the microgrid, the algorithm and implementation method for solving the model is designed on a distributed iterative way. Finally, through a practical case study, the effectiveness of the method is verified in terms of saving PV prosumers' costs and improving the sharing of the PV energy.
Peak demand on electricity grids is a growing problem that increases costs and risks to supply security. Residential sector loads often contribute significantly to seasonal and daily peak demand. ...Demand response projects aim to manage peak demand by applying price signals and automated load shedding technologies. This research investigates voluntary load shedding in response to information about the security of supply, the emission profile and the cost of meeting critical peak demand in the customers’ network. Customer willingness to change behaviour in response to this information was explored through mail-back survey. The diversified demand modelling method was used along with energy audit data to estimate the potential peak load reduction resulting from the voluntary demand response. A case study was conducted in a suburb of Christchurch, New Zealand, where electricity is the main source for water and space heating. On this network, all water heating cylinders have ripple-control technology and about 50% of the households subscribe to differential day/night pricing plan. The survey results show that the sensitivity to supply security is on par with price, with the emission sensitivity being slightly weaker. The modelling results show potential 10% reduction in critical peak load for aggregate voluntary demand response.
► Multiple-factor behaviour intervention is necessarily for effective residential demand response. ► Security signals can achieve result comparable to price. ► The modelling results show potential 10% reduction in critical peak load for aggregate voluntary demand response. ► New Zealand’s energy policy should include innovation and development of VDR programmes and technologies.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
We consider an on-demand service platform using earning-sensitive independent providers with heterogeneous reservation price (for work participation) to serve its time and price-sensitive customers ...with heterogeneous valuation of the service. As such, the supply and demand are “endogenously” dependent on the price the platform charges its customers and the wage the platform pays its independent providers. We present an analytical model with endogenous supply (number of participating agents) and endogenous demand (customer request rate) to study this on-demand service platform. To coordinate endogenous demand with endogenous supply, we include the steady-state waiting time performance based on a queueing model in the customer utility function to characterize the optimal price and wage rates that maximize the profit of the platform. We first analyze a base model that uses a fixed payout ratio (i.e., the ratio of wage over price), and then extend our model to allow the platform to adopt a time-based payout ratio. We find that it is optimal for the platform to charge a higher price when demand increases; however, the optimal price is not necessarily monotonic when the provider capacity or the waiting cost increases. Furthermore, the platform should offer a higher payout ratio as demand increases, capacity decreases or customers become more sensitive to waiting time. We also find that the platform should lower its payout ratio as it grows with the number of providers and customer demand increasing at about the same rate. We use a set of actual data from a large on-demand ride-hailing platform to calibrate our model parameters in numerical experiments to illustrate some of our main insights.
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CEKLJ, IZUM, KILJ, NUK, PILJ, SAZU, UL, UM, UPUK
The pattern of electricity use in an individual domestic dwelling is highly dependent upon the activities of the occupants and their associated use of electrical appliances. This paper presents a ...high-resolution model of domestic electricity use that is based upon a combination of patterns of active occupancy (i.e. when people are at home and awake), and daily activity profiles that characterise how people spend their time performing certain activities. One-min resolution synthetic electricity demand data is created through the simulation of appliance use; the model covers all major appliances commonly found in the domestic environment. In order to validate the model, electricity demand was recorded over the period of a year within 22 dwellings in the East Midlands, UK. A thorough quantitative comparison is made between the synthetic and measured data sets, showing them to have similar statistical characteristics. A freely downloadable example of the model is made available and may be configured to the particular requirements of users or incorporated into other models.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
•Provide a 50th anniversary bibliometric review of the bottleneck model research;.•Identify influential papers, top contributing authors and leading topics;.•Review the studies in terms of travel ...behavior, demand-side and supply-side strategies;.•Discuss potential directions for further studies.
The bottleneck model introduced by Vickrey in 1969 has been recognized as a benchmark representation of the peak-period traffic congestion due to its ability to capture the essence of congestion dynamics in a simple and tractable way. This paper aims to provide a 50th anniversary review of the bottleneck model research since its inception. A bibliometric analysis approach is adopted for identifying the distribution of all journal publications, influential papers, top contributing authors, and leading topics in the past half century. The literature is classified according to recurring themes into travel behavior analysis, demand-side strategies, supply-side strategies, and joint strategies of demand and supply sides. For each theme, typical extended models developed to date are surveyed. Some potential directions for further studies are discussed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
We study the residential demand for electricity and gas, working with nationwide household-level data that cover recent years, namely 1997–2007. Our dataset is a mixed panel/multi-year cross-sections ...of dwellings/households in the 50 largest metropolitan areas in the United States as of 2008. We estimate static and dynamic models of electricity and gas demand. We find strong household response to energy prices, both in the short and long term. From the static models, we get estimates of the own price elasticity of electricity demand in the −
0.860 to −
0.667 range, while the own price elasticity of gas demand is −
0.693 to −
0.566. These results are robust to a variety of checks. Contrary to earlier literature (Metcalf and Hassett, 1999; Reiss and White, 2005), we find no evidence of significantly different elasticities across households with electric and gas heat. The price elasticity of electricity demand declines with income, but the magnitude of this effect is small. These results are in sharp contrast to much of the literature on residential energy consumption in the United States, and with the figures used in current government agency practice. Our results suggest that there might be greater potential for policies which affect energy price than may have been previously appreciated.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
This paper examines the advantages and drawbacks of alternative methods of estimating oil supply and oil demand elasticities and of incorporating this information into structural VAR models of the ...global oil market. I show that some of these methodologies suffer from drawbacks that call into question the estimates they generate. I also explain the rationale for the use of alternative elasticity definitions in the literature and discuss the trade-off between these definitions. Once these issues are recognized, seemingly conflicting conclusions in the recent literature can be reconciled. My analysis reaffirms the conclusion that the one-month oil supply elasticity is close to zero, which implies that oil demand shocks are the dominant driver of the real price of oil.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Within the next years, consumer households will be increasingly equipped with smart metering and intelligent appliances. These technologies are the basis for households to better monitor electricity ...consumption and to actively control loads in private homes. Demand side management (DSM) can be adopted to private households. We present a simulation model that generates household load profiles under flat tariffs and simulates changes in these profiles when households are equipped with smart appliances and face time-based electricity prices.
We investigate the impact of smart appliances and variable prices on electricity bills of a household. We show that for households the savings from equipping them with smart appliances are moderate compared to the required investment. This finding is quite robust with respect to variation of tariff price spreads and to different types of appliance utilization patterns.
Finally, our results indicate that electric utilities may face new demand peaks when day-ahead hourly prices are applied. However, a considerable amount of residential load is available for shifting, which is interesting for the utilities to balance demand and supply.
► Our model generates residential load profiles that are based on real world data. ► We simulate changes in load profiles when smart appliances and time-of-use tariffs are applied. ► The economic incentive for households to invest in smart appliances is low. ► Time-of-use tariffs create new, even higher peaks. ► Electric utilities have a large amount of the hourly load available for shifting.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Large customers in many electric distribution utilities must enter into demand contracts for the ensuing year for defining contracted demand. Customer demand charge equals contracted demand billed at ...contracted tariff if the peak demand is less than the contracted demand, and, if not, the excess is billed at the uncontracted tariff. Both scenarios lead to economic loss for the customer, as the uncontracted tariff is much higher than the contracted tariff. Further, optimization of demand contracts is also important for utilities, as they plan and operate their system to satisfy customer peak demand. If under planned, it leads to technical challenges, and otherwise, it leads to economic loss. This challenge of determining the best demand to be contracted is known as the demand cost optimization problem and would save US$ 38 billion globally to customers. This work describes the problem through a graphical approach and proposes three mathematical models to find the optimum demand even in the presence of intermittent renewable generation. Each model is verified through a case study and an exhaustive study with 7,000 large customers from a Brazilian utility. The formulations are easily implementable and have the potential to assist large customers and utilities with planning studies.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
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