Daily Peak Demand Research Articles

A demand limiting strategy for maximizing monthly cost savings of commercial buildings

Monthly peak demand costs usually contribute greatly to the monthly electricity bills of commercial buildings. Peak demand limiting control, which gained substantial attention recently, is an efficient way to reduce it. Most of previous studies either focus on the daily peak demand reduction without taking account the related energy rise, or explore the relationship between energy rise and demand reduction only on a daily basis. Unlike the previous studies, a new demand limiting control strategy is proposed in this paper in order to maximize the monthly cost saving. The new strategy is realized as follows. At first step, a proper monthly demand threshold is identified. At second step, a specific approach, named as proportional-integral-derivative (PID) algorithm, is implemented to restrain the daily peak demand to the given threshold by adjusting the indoor room temperature set-point. The extended pre-cooling duration is also estimated at this step based on the difference between the predicted daily peak demand and the identified threshold. The results of case studies show that the proposed strategy can substantially reduce the monthly electricity cost.

Evaluation of Regional kW-Value of PV System based on Past Records of Electricity Load and Insolation

We have proposed a method to evaluate the kW-value of PV system, where the kW-value is calculated as an average value of reduction in peak electricity load on the electric power utility among a few days with large peak load in a year. By using the proposed method, this paper discusses the difference in kW-value of PV system due to the difference in region considered. The evaluation is based on the actual insolation data in 45 points throughout Japan and electricity load on the electric power utility both observed in the 1990's. As results, the number of days with large reduction in peak load by PV systems is larger in Western Japan, because the solar noon is slightly late in Western Japan, resulting in the large electricity supply of PV system during the period of large electricity load. Therefore, when the kW-value is evaluated based on a number of days with large daily peak load, the kW-value of PV system would be higher in Shikoku and Kyusyu regions. However, when the kW-value is evaluated based on a number of days with large daily peak demand but with small insolation, the evaluated kW-value is not so different among regions in Japan, because the ratio of such the day is not so different among regions.

Benefits of Peak Load Reduction Resulting from the Central Air-Conditioning (CAC) Standard

The benefits of reducing peak electricity demand could be one of the more important considerations in the current rulemaking for heat pumps and central air conditioners (CAC). The benefits are twofold: first, the direct cost savings from avoided generation and plant construction needed to meet peak loads, and second, the reduced risk of system outage and its consequent societal disruption. This analysis estimates the reduction in generator outages (i.e., unscheduled plant and excessive demand-related outages) due to the proposed CAC standards, and the value of the standards for three NERC regions: the New England Power Pool (NEPOOL), the California Independent System Operator (CA ISO), and the Pennsylvania-New Jersey-Maryland (PJM) region. The analysis does not consider supply disruptions caused by security failures, transmission and distribution failures, or extreme weather events. Stratus Consulting provided the Ernest Orlando Lawrence Berkeley National Laboratory (LBNL) with load curves for the three regions analyzed, and LBNL determined the impact of the proposed CAC standards on the load curves. Using a Monte Carlo simulation based on historical capacity and demand data, Stratus Consulting modeled the loss of load expectation (LOLE) - the expected number of days in a year when the daily peak demand exceeds the available generating capacity - for each region with and without the proposed CAC standards. The difference in LOLE represents the impact of CAC standards on reducing the likelihood of generator outages. To value the proposed CAC standards, we estimated the value of the reduced energy consumed that results from the standards. The Stratus Asset Modeler (SAM) was used to estimate electricity prices for each region. SAM is a mean reverting model with jumps that captures the volatility in spot prices seen in deregulated markets. The model determines super-peak, peak, sub-shoulder peak, and off-peak prices for each month. SAM, which relies on market fundamentals in its price estimation, incorporates business cycles throughout the reserve margin forecast because industries such as electricity generation, with long lead times, exhibit periods of low returns followed by periods of high returns. Our analysis found that the proposed new CAC standards will reduce generator outages by 6% 12% in 2010. The proposed CAC standards yield a 19% to 20% reduction in outages in 2020 because tighter reserve margins are forecast. The proposed standards are valued at $3.9 to$14.5 million in 2010 and at $15.2 to $43.5 million in 2020.

Flow control by prediction of water demand

The production rate of drinking water treatment plants in the Netherlands is currently often directly related to the level in the clear water reservoir. This results in many production rate changes, and the daily peak demands are not completely levelled off in the clear water reservoir. A recently developed control algorithm, based on an adaptive demand prediction, can achieve a more stable production pattern with a minimum of flow changes. The implementation of the algorithm at several treatment plants has led to a significant optimisation of the treatment process. A more stable treatment process leads to better clear water quality and to less energy consumption. A future combination of a control algorithm and a qualitative model opens up the possibilities for further optimisation of the treatment process and to a consistent clear water quality.

KW‐value of a photovoltaic power generation system evaluated from actual data on electricity demand and insolation

AbstractThis paper proposes a new method of evaluating the kW‐value of photovoltaic power generation systems (PV systems). This evaluation is based on actual insolation data, collected by the Japan Meteorological Agency at four locations in the Chubu area in Japan, and the actual electrical load data of the Chubu area in the 1990s. We selected specific days on which the daily peak load was relatively high. For the selected days, we estimated the average output of PV systems using the IEA's method for considering the temperature dependency of cell efficiency. Then, we calculated the reduction of the peak load ΔLp(d). The resultant ΔLp(d) was distributed in the range from 0.4 to 0.5 kW/kWp.We evaluated the average value of the smallest ΔLp(d) as the kW‐value of PV systems. When the kW‐value was evaluated for the two or three days with the smallest ΔLp(d) among the selected 10 days for 1 year, the kW‐value of the PV systems was calculated to be about 30%. This was close to the value obtained by the conventional loss of load probability (LOLP) method. Our study evaluates the relationship between the kW‐value of PV systems and the number of days on which the insolation was low but the daily peak demand was relatively high. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 144(1): 42–49, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10089

Utilization of wind energy in space heating and cooling with hybrid HVAC systems and heat pumps

Approximately one-third of the primary energy resources are consumed in space heating, cooling, and air-conditioning with a very low exergetic efficiency. The depleting nature of primary energy resources, negative environmental impact of fossil fuels and low exergetic efficiencies obtained in conventional space heating and cooling are the main incentives for developing alternative heating, ventilating, and air-conditioning (HVAC) techniques which can employ low density and interrupted energy sources. In this respect, in spite of difficulties primarily encountered in coupling wind energy with conventional space heating and cooling equipment, wind energy seems to be an exciting alternative provided that synectic combinations are pursued and applied. In this paper, a new wind turbine coupled hybrid HVAC system is presented, which consists of an optimum combination of convective and radiant heating and cooling systems with in-space thermal energy storage. A design case for a single family home is presented. In this study a 6 kW(e) wind turbine drives a ground source heat pump (GSHP) which is coupled to a hybrid HVAC system to satisfy the thermal loads of a 100 m 2 home. In this example, sensible heating and cooling loads are satisfied by the high mass radiant floor which matches the daily peak demand and the available peak wind energy. Latent heating and cooling loads, along with ventilation requirements are satisfied by a forced-air system. Variable radiant and convective split type of control is implemented, and both systems are served by the same GSHP which also satisfies the domestic hot water (DHW) demand.

Metered residential cooling loads: comparison of three models

End-use metered data collected for five years from 350 California residences are used to compare three types of models for allocating estimates of annual residential central air conditioning energy use to hours of the year. We assess how well the model fits the data for daily energy, peak demand, and demand coincident with system peak. A model which couples regression-based functions for daily load estimation with hourly estimation according to a library of load profiles is judged to have a slightly better fit to the data than a model that estimates hourly loads directly from hourly functions derived from linear regressions. Concerns regarding the applicability of end-use metered data for long-term resource planning are described.

Electric Demands and Energy Usage of Rural Residential Customers in Kansas

ABSTRACT Electric demand and electric energy use data from 83 rural residential customers in Kansas were gathered from May 1982 to May 1984. the customers were statistically selected after stratification of all rural residential customers into one of three usage categories; low, medium, or high, based on their energy use during the power suppliers peak month during 1981. Regression analyses were used to determine correlations between coincidental daily demand and energy use for each category. A high correlation was found for coincidental peak, average, and minimum daily demand and energy use in each category. Frequency distributions for coin-cidental daily peak demand, coincidental daily energy use, and daily peak time are presented for each category. Median demands were 1.0, 1.7 and 2.6 kW for the low, medium, and high usage categories, respectively.

A study on the accuracy of time series modeling of daily power demand

The suitability and reliability of the stochastic time series modeling and forecasting approach to load management are examined through actual applications not only to aggregate system load but also to a variety of load types at the 66 22 kV level representing electricity demands in areas of several km 2. Daily peak demand forecasts with an accuracy comparable with that of telemetering values have been found to be attainable irrespective of load type, thus establishing the practicality and usefulness of the methodology to operations management and network planning.

Improving the match between the daily solar insolation peak and electrical peak demand

Within the next decade photovoltaic solar systems will be used more extensively for generating electricity. The value of that electricity, and the system is directly related to the degree to which its availability coincides with the customers' peak demand. For most utilities and other grid-connected users the peak demand is in the afternoon while peak solar insolation occurs at noon. This paper describes a method of attaining coincidence of the energy collected with the demand for energy. The approach used is to vary the azimuth angle of the collector panel, then determine the corresponding time shift in the peak collection point along with the total energy collected. Results show a 60° shift in azimuth has a corresponding time shift of 1 hr and 40 min in the summer with a small gain in total energy collected. However, the winter time shift is only 1 hr, 20 min and the loss of total energy is 28 per cent. A significant improvement is made to match the demand peak by varying the azimuth angle of the solar collector with a total energy loss that may be acceptable for many users. The results presented here can be used to maximize the value of the photovoltaic systems to specific utilities and other grid-connected users.