Search results

The main purpose of this study is to provide an effective sizing method and an optimal peak shaving strategy for an energy storage system to reduce the electrical peak demand of the customers. A cost-savings analytical tool is developed to provide a quick rule-of-thumb for customers to choose an appropriate size of energy storage for various tariff schemes.

A novel sizing method is proposed to obtain the optimum size of energy storage for commercial and industrial customers based on their historical load profile. An algorithm is developed to determine the threshold level for peak shaving. One of the buildings at Universiti Tunku Abdul Rahman (UTAR), Malaysia, is chosen for this study. A three-phase energy storage system rated at 15 kVA is developed and connected to the low-voltage electrical network in the building. An adaptive control algorithm is developed and implemented to optimize the peak shaving.

The sizing analysis shows that the customer under the C2 tariff rate yields the highest saving, followed by E2, C1 and E1. The experimental results presented indicate that the proposed adaptive control algorithm has effectively optimized the peak demand to be shaved.

This study demonstrates the potential of energy storage in reducing the peak demand and cost of electricity. One of the main challenges of real-time peak shaving is to determine an appropriate threshold level such that the energy stored in the energy storage system is sufficient during the peak shaving process.

The originality of the paper is the optimal sizing method of the energy storage system based on the historical load profile and adaptive control algorithm to optimize the peak demand deduction.

Agile manufacturers depend on low cost, abundant electricity to remain competitive in the global marketplace. Self‐generation of electricity or cogeneration of electricity and thermal energy at the manufacturer’s location can provide both economical and uninterrupted service. Generation methods are standby, peak‐shaving, baseload, commercial, and mobile generation. Each of these represents opportunities in agile manufacturing. The number of countries that have deregulated, market‐driven electrical utilities are growing and will include most of Europe and the USA by 2005. The demands of agile manufacturing are to produce high‐quality, market‐sensitive products at the lowest possible cost in an environment that has constant dynamic changes. Cogeneration will provide both flexible and cost‐efficient electricity as part of an overall energy strategy. Further, it will provide an agile energy resource that will complement the pursuit of competitive advantage in the global market for customized goods and services.

The purpose of this paper is to examine the feasibility of an ideal power network that combines many different renewable energy technologies such as wind power, concentrated solar power (CSP) and hydroelectric power. This paper emphasizes in finding the benefits arising from hydrothermal coordination compared to the non-regulated integration of the hydroelectric units, as well as the benefits from the integration of wind power and CSP.

Artificial Neural Networks were used to estimate wind power output. As for the CSP system, a three-tier architecture which includes a solar field, a transmission-storage system and a production unit was used. Each one of those separate sections is analyzed and the process is modeled. As for the hydroelectric plant, the knowledge of the water’s flow rated has helped estimating the power output, taking into account the technical restrictions and losses during transmission. Also, the economic dispatch problem was solved by using artificial intelligence methods.

Hydrothermal coordination leads to greater thermal participation reduction and cost reduction than a non-regulated integration of the hydrothermal unit. The latter is independent from the degree of integration of the other renewable sources (wind power, CSP).

Hydrothermal coordination in a power system which includes thermal units and CSP for cost and emissions reduction.

Rapidly increasing the proportion of installed wind power capacity with zero carbon emission characteristics will help adjust the energy structure and support the realization of carbon neutrality targets. The intermittency of wind resources and fluctuations in electricity demand has exacerbated the contradiction between power supply and demand. The time-of-use pricing and supply-side allocation of energy storage power stations will help “peak shaving and valley filling” and reduce the gap between power supply and demand. To this end, this paper constructs a decision-making model for the capacity investment of energy storage power stations under time-of-use pricing, which is intended to provide a reference for scientific decision-making on electricity prices and energy storage power station capacity.

Based on the research framework of time-of-use pricing, this paper constructs a profit-maximizing electricity price and capacity investment decision model of energy storage power station for flat pricing and time-of-use pricing respectively. In the process, this study considers the dual uncertain scenarios of intermittency of wind resources and random fluctuations in power demand.

(1) Investment in energy storage power stations is the optimal decision. Time-of-use pricing will reduce the optimal capacity of the energy storage power station. (2) The optimal capacity of the energy storage power station and optimal electricity price are related to factors such as the intermittency of wind resources, the unit investment cost, the price sensitivities of the demand, the proportion of time-of-use pricing and the thermal power price. (3) The carbon emission level is affected by the intermittency of wind resources, price sensitivities of the demand and the proportion of time-of-use pricing. Incentive policies can always reduce carbon emission levels.

This paper creatively introduced the research framework of time-of-use pricing into the capacity decision-making of energy storage power stations, and considering the influence of wind power intermittentness and power demand fluctuations, constructed the capacity investment decision model of energy storage power stations under different pricing methods, and compared the impact of pricing methods on optimal energy storage power station capacity and carbon emissions.

1. Electricity pricing and capacity of energy storage power stations in an uncertain electricity market.

2. Investment strategy of energy storage power stations on the supply side of wind power generators.

3. Impact of pricing method on the investment decisions of energy storage power stations.

4. Impact of pricing method, energy storage investment and incentive policies on carbon emissions.

5. A two-stage wind power supply chain including energy storage power stations.

Electricity pricing and capacity of energy storage power stations in an uncertain electricity market.

Investment strategy of energy storage power stations on the supply side of wind power generators.

Impact of pricing method on the investment decisions of energy storage power stations.

Impact of pricing method, energy storage investment and incentive policies on carbon emissions.

A two-stage wind power supply chain including energy storage power stations.

On‐site power has many potential benefits, from temporary relief to congested transmission and distribution systems, to alleviating problems created by incorrect load forecasts, all the way to meeting the load requirements of a facility. Many of these assets are being installed to take advantage of one or more of the above opportunities. This paper shows how effective utilisation of these assets not only increases the reliability of a facility, but also helps generate revenue from assets that would otherwise sit idle. It goes on to demonstrate to facility managers how they can successfully implement such a model through better understanding of their facilities’ needs combined with financial details of various programmes offered by local suppliers of electricity.

This study aims to form a new concept of power-to-gas-based virtual power plant (GVPP) and propose a low-carbon economic scheduling optimization model for GVPP considering carbon emission trading.

In view of the strong uncertainty of wind power and photovoltaic power generation in GVPP, the information gap decision theory (IGDT) is used to measure the uncertainty tolerance threshold under different expected target deviations of the decision-makers. To verify the feasibility and effectiveness of the proposed model, nine-node energy hub was selected as the simulation system.

GVPP can coordinate and optimize the output of electricity-to-gas and gas turbines according to the difference in gas and electricity prices in the electricity market and the natural gas market at different times. The IGDT method can be used to describe the impact of wind and solar uncertainty in GVPP. Carbon emission rights trading can increase the operating space of power to gas (P2G) and reduce the operating cost of GVPP.

This study considers the electrical conversion and spatio-temporal calming characteristics of P2G, integrates it with VPP into GVPP and uses the IGDT method to describe the impact of wind and solar uncertainty and then proposes a GVPP near-zero carbon random scheduling optimization model based on IGDT.

This study designed a novel structure of the GVPP integrating P2G, gas storage device into the VPP and proposed a basic near-zero carbon scheduling optimization model for GVPP under the optimization goal of minimizing operating costs. At last, this study constructed a stochastic scheduling optimization model for GVPP.

Suggests that both future supplies of, and the future demand for, North Sea Gas are highly uncertain. Gives examples to show that one can argue, with equal plausibility, that in the 1970s there could be either a significant shortage, or a substantial surplus of capacity relative to ‘premium’ gas demand. Argues that the uncertainty of the future demands a highly flexible marketing policy in which tariffs, with built‐in incentives to improve load factors, are aimed at keeping the market in balance and interruptible sales are one of the main marketing weapons. Sums up that this paper has tried to establish some guidelines for natural gas marketing policy in this country, working within some of the constraints which have already been established.

Flood season in our country is characterized by frequent heavy rains, and flood problems are becoming increasingly serious. The uneven distribution of water resources causes conflicts in the occurrence of floods and droughts. Implementing effective flood control planning and solving drought and flood disasters are the research highlights of relevant institutions both domestic and abroad. This study develops a multiscale method of urban flood control planning based on microcirculation. A microcirculation water ecosystem, which consists of six elements, namely, collecting, interacting, precipitating, reserving, storing, and purifying, is introduced. This study investigates precipitation; peak shaving; recycle mode of filtration at the macro level in different regions; “hierarchy” in rainwater ecosystems in rain parks, heavy rain garden parks, and wetland parks at the meso level; and the concept of zero-emission rain in residential areas and roads at the micro level. Finally, this study analyzes a rain garden and its domestic application. A conclusion is drawn that the flood control planning model based on microcirculation can effectively reduce rain runoff. Empirical measurement proves that the proposed multiscale model for city flood control planning based on microcirculation promotes flood control and effectively reduces the occurrence of droughts and floods.

Some of the major concerns since the implementation of smart meters (prepaid meters) in some parts of Ghana is how electricity consumers have benefited from data obtained from these meters by providing important statistics on electricity-saving advice; this is one of the key demand-side management methods for achieving load reduction in residential homes. Appliance shifting techniques have proved to be an effective demand response strategy in load reduction. The purpose of this paper is therefore to help consumers of electricity understand when and how they can shift some appliances from peak to off-peak and vice versa.

The research uses an analysis technique of Richardson et al. (2010). In their survey on time-of-use surveys to determine the usage of electricity in households as far as appliance shifting was concerned, this study allowed for the assessment of how the occupants’ daily activities in households affect residential electricity consumption. Fell et al. (2014) modeled an aggregate of electricity demand using different appliances (n) in the household. The data for the peak time used in this study were identified from 05:00 to 08:00 and 17:00 to 21:00 for testing the load shifting algorithms, and the off-peak times were pecked from 10:00 to 16:00 and 23:00. This study technique used load management considering real-time scheduling for peak levels in the selected homes. The household devices are modeled in terms of controlled parameters. Using this study’s time-triggered loads on refrigerators and air conditioning systems, the findings suggested that peak loads can be reduced to 45% as a means of maintaining the simultaneous quality of service. To minimize peak loads to around 35% or more, Chaiwongsa and Wongwises (2020) have indicated that room air conditioning and refrigerator loads are simpler to move compared to other household appliances such as cooking appliances. Yet in conclusion, this study made a strong case that a decrease in household peak demand for electricity is primarily contingent on improvements in human behavior.

This study has shown that appliance load shifting is a very good way of reducing electrical consumption in residential homes. The comparative performance shows a moderate reduction of 1% in load as was found in the work done by Laicaine (2014). The results, however, indicate that load shifting to a large extent can be achieved by consumer behavioral change. The main response to this study is to advise policymakers in Ghana to develop the appropriate demand response and consumer education towards the general reduction in electrical load in domestic households. The difficulty, however, is how to get the attention of consumer’s on how to start using appliances with less load at peak and also shift some appliances from off-peak times. By increasing consumer knowledge and participation in demand response, it is possible to achieve more efficiency and flexibility in load reduction. The findings were benchmarked with existing comparison studies but may benefit from the potential production of structured references. However, the findings show that load shifting can only be done by modifying consumer actions.

It should be remembered that this study showed that the use of appliances shifting in residential homes results in load reduction benefits for customers, expressed as savings in electricity prices. The next step will be to build on this cost/benefit study to explain and measure how these reductions transform into net consumer gains for all Ghanaian households.

Load shifting will include load controllers in the future, which would automatically handle electricity consumption from various appliances in the home. Based on the device and user needs, the controllers can prioritize loads and appliance usage. The algorithms that underpin automatic load controllers will include knowledge about the behaviors of groups of end users. The results on the time dependency of activities may theoretically inform the algorithms of automatic demand controllers.

This paper addresses an important need for the country in the midst of finding solutions to an unending energy crisis. This paper presents demand response to the Ghanaian electricity consumer as a means to help in the reduction of load in residential homes. This is a novel research as no one has at yet carried out any research in this direction in Ghana. This paper has some new information to offer in the field of demand in household electricity consumption.

The purpose of the paper is to analyze the effect on centralized dispatching generation cost under the condition where the single‐buyer electric supply industry (ESI) with independent power producer (IPP) scheme (the ESI structure that is widely implemented in developing countries) is opened for bilateral trading. The analysis is based on the Thai power system.

The analysis considers the average generation cost (B/kWh) derived from unit commitment of power generation under three cases – single‐buyer model with must‐run IPP scheme, unconstrained operation case, and the case where bilateral trading is introduced. The analysis is performed for different demand levels.

The results indicate that the operational constraint from the virtual must‐run power purchase agreement under IPP scheme leads to higher generation cost. The choice of allowing IPP to trade through bilateral trading and removal of the must‐run contract shows potential to lessen the operational constraint and lower generation cost can be achieved under some conditions – depending on the plant type and the share of bilateral market in the system. The planning and policy should take into consideration these conditions especially during the transitional period of ESI reform.

The main limitation of the analysis is the availability to recent data. The load factor of the demand curve is taken from the peak day of the year, resulting in higher load factor than the average of Thailand. With lower load factor, the must‐run constraints might be more obvious during the lighter load day and more expensive generation cost can be observed. However, the cases are compared at same demand curve. Therefore, the trend of result will lead to the same conclusion.

Uneconomic operation of the single‐buyer ESI with IPP scheme which has been implemented in many developing countries was clearly determined. The literature shows that the ESI operation can be more efficient when the sector moves towards higher degree of competition, either fully competitive market or bilateral trading. The potential for better operating conditions for bilateral trading has been suggested. The simulation based on the power system of Thailand can be an example for other developing countries operating under the similar ESI structure.