79 research outputs found

    Cyclic blackout mitigation and prevention

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    Severe and long-lasting power shortages plague many countries, resulting in cyclic blackouts affecting the life of millions of people. This research focuses on the design, development and evolution of a computer-controlled system for chronic cyclic blackouts mitigation based on the use of an agent-based distributed power management system integrating Supply Demand Matching (SDM) with the dynamic management of Heat, Ventilation, and Air Conditioning (HVAC) appliances. The principle is supported through interlocking different types of HVAC appliances within an adaptive cluster, the composition of which is dynamically updated according to the level of power secured from aggregating the surplus power from underutilised standby generation which is assumed to be changing throughout the day. The surplus power aggregation provides a dynamically changing flow, used to power a basic set of appliances and one HVAC per household. The proposed solution has two modes, cyclic blackout mitigation and prevention modes, selecting either one depends on the size of the power shortage. If the power shortage is severe, the system works in its cyclic blackout mitigation mode during the power OFF periods of a cyclic blackout. The system changes the composition of the HVAC cluster so that its demand added to the demand of basic household appliances matches the amount of secured supply. The system provides the best possible air conditioning/cooling service and distributes the usage right and duration of each type of HVAC appliance either equally among all houses or according to house temperature. However if the power shortage is limited and centred around the peak, the system works in its prevention mode, in such case, the system trades a minimum number of operational air conditioners (ACs) with air cooling counterparts in so doing reducing the overall demand. The solution assumes the use of a new breed of smart meters, suggested in this research, capable of dynamically rationing power provided to each household through a centrally specified power allocation for each family. This smart meter dynamically monitors each customer’s demand and ensures their allocation is never exceeded. The system implementation is evaluated utilising input power usage patterns collected through a field survey conducted in a residential quarter in Basra City, Iraq. The results of the mapping formed the foundation for a residential demand generator integrated in a custom platform (DDSM-IDEA) built as the development environment dedicated for implementing and evaluating the power management strategies. Simulation results show that the proposed solution provides an equitably distributed, comfortable quality of life level during cyclic blackout periods.Severe and long-lasting power shortages plague many countries, resulting in cyclic blackouts affecting the life of millions of people. This research focuses on the design, development and evolution of a computer-controlled system for chronic cyclic blackouts mitigation based on the use of an agent-based distributed power management system integrating Supply Demand Matching (SDM) with the dynamic management of Heat, Ventilation, and Air Conditioning (HVAC) appliances. The principle is supported through interlocking different types of HVAC appliances within an adaptive cluster, the composition of which is dynamically updated according to the level of power secured from aggregating the surplus power from underutilised standby generation which is assumed to be changing throughout the day. The surplus power aggregation provides a dynamically changing flow, used to power a basic set of appliances and one HVAC per household. The proposed solution has two modes, cyclic blackout mitigation and prevention modes, selecting either one depends on the size of the power shortage. If the power shortage is severe, the system works in its cyclic blackout mitigation mode during the power OFF periods of a cyclic blackout. The system changes the composition of the HVAC cluster so that its demand added to the demand of basic household appliances matches the amount of secured supply. The system provides the best possible air conditioning/cooling service and distributes the usage right and duration of each type of HVAC appliance either equally among all houses or according to house temperature. However if the power shortage is limited and centred around the peak, the system works in its prevention mode, in such case, the system trades a minimum number of operational air conditioners (ACs) with air cooling counterparts in so doing reducing the overall demand. The solution assumes the use of a new breed of smart meters, suggested in this research, capable of dynamically rationing power provided to each household through a centrally specified power allocation for each family. This smart meter dynamically monitors each customer’s demand and ensures their allocation is never exceeded. The system implementation is evaluated utilising input power usage patterns collected through a field survey conducted in a residential quarter in Basra City, Iraq. The results of the mapping formed the foundation for a residential demand generator integrated in a custom platform (DDSM-IDEA) built as the development environment dedicated for implementing and evaluating the power management strategies. Simulation results show that the proposed solution provides an equitably distributed, comfortable quality of life level during cyclic blackout periods

    Microgrid Energy Management with Flexibility Constraints: A Data-Driven Solution Method

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    Microgrid energy management is a challenging and important problem in modern power systems. Several deterministic and stochastic models have been proposed in the literature for the microgrid energy management problem. However, more accurate models are required to enhance flexibility of the microgrids when accounting for renewable energy and load uncertainties. This thesis proposes key contributions to solve the energy management problem for smart building (or small-scale microgrid). In Chapter 3, a deterministic energy management model is presented taking into account system flexibility requirements. Energy storage systems are deployed to enhance the grid flexibility and ramping capability. The objective function of the formulated optimization is to minimize the operation cost. Combined heat and power (CHP) units, which interconnect heat and electricity, are modeled. Thus, electricity and thermal generation and load constraints are formulated. To account for uncertainties of load and renewable energy resources (e.g., solar generation), a stochastic energy management model is proposed in Chapter 4. A data-driven chance-constrained optimization is based method is formulated. The proposed model is nonparametric that imposes no assumption on probability distribution functions (PDFs) of the random variables (i.e., load and renewable generation). Adaptive kernel density estimation is deployed to estimate a nonparametric PDF for each random variable. Confidence levels (risk levels) of the chance constraints are modified according to estimation errors. Several cases are simulated to analyze the deterministic and stochastic optimization models. The simulation results show that the proposed data-driven chance-constrained optimization with the flexibility constraints enhance reliability, resiliency, and economics of the microgrid energy systems. Note that these flexibility constraints avoid propagating solar and load fluctuations to the distribution feeder. That is smart building (microgrid) is capable of capturing fluctuations locally

    The Queensland community’s propensity to invest in the resilience of their community and the electrical distribution network

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    Electricity supply is vital for community response and recovery in the aftermath of a disaster. Everything from disaster response coordination, communication, public lighting and safety, as well as the provision of health services, basic household operations and the economic recovery of the community, relies on electricity to function. This dependency, coupled with the vulnerability of our electricity networks, highlights the need to establish resilient distribution networks. The notion that small-scale solar PV (SSPV) and battery energy storage systems (BESS) might contribute to network resilience, has become a popular avenue of investigation, with the growing uptake of these technologies. Beyond the technical challenges of establishing a smart grid network and reaching the required uptake of the technology to have sufficient storage capacity, a third factor relating to householders’ willingness to share stored energy with their community, remains largely unexplored. In a marked departure from the existing literature, this thesis investigates the use of SSPV and BESS for distribution network resilience and the community’s attitudes towards sharing energy resources. The research focusses, not on the technical and regulatory aspects of network resilience which are favoured by researchers’, but the behavioural component founded in social sciences. A model for network resilience utilising SSPV and BESS is presented, which argues that a key component of resilience in the aftermath of a disaster event, hinges on the community’s commitment to conservation of energy resources and their willingness to share their stored reserves for the common good. This research investigates the community’s perspectives on this resilience approach, by exploring attitudinal and behavioural aspects associated with helping the community, to determine the viability of pursuing SSPV and BESS as a practical network resilience option

    Demand-Response in Smart Buildings

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    This book represents the Special Issue of Energies, entitled “Demand-Response in Smart Buildings”, that was published in the section “Energy and Buildings”. This Special Issue is a collection of original scientific contributions and review papers that deal with smart buildings and communities. Demand response (DR) offers the capability to apply changes in the energy usage of consumers—from their normal consumption patterns—in response to changes in energy pricing over time. This leads to a lower energy demand during peak hours or during periods when an electricity grid’s reliability is put at risk. Therefore, demand response is a reduction in demand designed to reduce peak load or avoid system emergencies. Hence, demand response can be more cost-effective than adding generation capabilities to meet the peak and/or occasional demand spikes. The underlying objective of DR is to actively engage customers in modifying their consumption in response to pricing signals. Demand response is expected to increase energy market efficiency and the security of supply, which will ultimately benefit customers by way of options for managing their electricity costs leading to reduced environmental impact

    Improving the intersect of the power distribution system and the built environment in developing countries

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    Power distribution systems, specifically where they intersect with the built environment, are highly underemphasised versus generation in power planning. In a time of technology advances and cost declines in distribution automation and related technologies, this is an area of high potential for improving energy efficiency. This is particularly of impact in developing countries where urbanisation is rapidly increasing. Evidence shows that the same missed opportunities and sub-optimal distribution planning techniques are repeatedly found across multiple geographies. In this research, tools were developed to rank these problems and create solutions. These tools were endorsed by power industry executives from three countries. Following this, the tools were applied in a developing corridor near the Thailand-Cambodia border where power density is increasing, in order to develop power system solutions for live infrastructure projects. The solutions include technologies such as distributed generation, microgrids, digital monitoring systems, CCHP units, and power storage. The solutions from the live example were then honed and endorsed in an interview with Thai power sector experts. The final research and tools developed were confirmed capable of producing actionable solutions for planners across the public and private sectors, who focus on power distribution in urbanising, developing counties

    系統電力の安定化と炭素排出削減に焦点を当てた分散型エネルギーシステムの多基準評価に関する研究

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    Distributed energy systems can save energy cost, reduce environmental impact and improve the reliability of the power grid. However, its high investment and improper capacity caused poor economic benefits. Moreover, the current evaluation method with a single criterion is relatively simple and one-sided, which cannot reflect the comprehensive benefits of the DES. Therefore, this research proposed a distributed energy system (DES) composed of photovoltaic, energy storage and gas engine, and its grid stabilization and carbon reduction potentials were analyzed. Focusing on these advantages, a multi-criteria evaluation method was established to optimize the system. Finally, different case study scenarios of the DES utilization were demonstrated. It is hoped to improve the core competitiveness of the DES and promote its development.北九州市立大

    Provision of Flexibility Services by Industrial Energy Systems

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    Residential Demand Side Management model, optimization and future perspective: A review

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    The residential load sector plays a vital role in terms of its impact on overall power balance, stability, and efficient power management. However, the load dynamics of the energy demand of residential users are always nonlinear, uncontrollable, and inelastic concerning power grid regulation and management. The integration of distributed generations (DGs) and advancement of information and communication technology (ICT) even though handles the related issues and challenges up to some extent, till the flexibility, energy management and scheduling with better planning are necessary for the residential sector to achieve better grid stability and efficiency. To address these issues, it is indispensable to analyze the demand-side management (DSM) for the complex residential sector considering various operational constraints, objectives, identifying various factors that affect better planning, scheduling, and management, to project the key features of various approaches and possible future research directions. This review has been done based on the related literature to focus on modeling, optimization methods, major objectives, system operation constraints, dominating factors impacting overall system operation, and possible solutions enhancing residential DSM operation. Gaps in future research and possible prospects have been discussed briefly to give a proper insight into the current implementation of DSM. This extensive review of residential DSM will help all the researchers in this area to innovate better energy management strategies and reduce the effect of system uncertainties, variations, and constraints
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