Hierarchical energy management system for controlling distributed energy resources in a community microgrid

Community Energy Systems (CES) can be used to unlock the potential of Distributed Energy Resources (DERs), maximize the local consumption of Renewable Energy Resources (RES) at the lowest level of electricity grid, and offer collective benefits to the end-users involved. If different electricity producers and consumers (prosumers) are connected to form a CES, the economic behaviour of the system needs to be fully understood. Therefore, a high priority in this important area is the development of a novel design procedure which allows the comprehensive and analytical investigation of the CES using integrating control, management strategies, optimal planning and scheduling and sizing procedures. This thesis presents novel centralized and decentralized hierarchical Community Energy Management Systems (CEMSs) which facilitate energy trading between prosumers in the CES by coordinating the operation of energy resources such as distributed or centralized battery energy storage and shiftable home appliances (located in each house) to achieve a further reduction in the daily household energy costs for each house, compared to being operated individually (i.e. not a part of the CES). The hierarchical CEMS represents an optimization-based real-time interactive algorithm which uses a combination of a Peer-to-Peer (P2P) energy trading scheme and a hierarchical optimization and control framework. This hierarchical CEMS reduces energy costs for end-users, maximizes self-consumption of locally generated energy, reduces the dependency of the CES on the main electrical grid, and reshapes the consumption profile of the CES to reduce peak consumption, while taking into account the battery degradation costs and the use of Demand Side Management (DSM) techniques. The novel structure of the hierarchical CEMS enables the algorithm to deal with frequent changes in the system using a short sample time. Detailed analysis of the performance of the household energy system using a real historic data of several UK households was performed to compare between end-users acting individually or as members of a CES. The performance of the household energy system is also assessed using different factors such as the overnight charging level, forecasting uncertainty, control sample time and tariff policies. Finally, a novel sizing methodology (in terms of energy and power rating) for a Community Battery Energy Storage System (CBESS) to provide Community Bill Management service plus addition ancillary services for the electricity/energy markets is presented. This includes an economic study to investigate if addition revenue could be obtained if the CBESS is used to provide more than one service. The results show the importance of participation in energy trading systems and the advantages of being a member of a CES, the need for using a centralized or a decentralized CEMS in coordination with energy trading systems to tackle the technical problems that may arise, and the importance of participation of the CES in the electricity market to achieve an appropriate return on investment

Real-Time Energy Management for a Small Scale PV-Battery Microgrid: Modeling, Design, and Experimental Verification

A new energy management system (EMS) is presented for small scale microgrids (MGs). The proposed EMS focuses on minimizing the daily cost of the energy drawn by the MG from the main electrical grid and increasing the self-consumption of local renewable energy resources (RES). This is achieved by determining the appropriate reference value for the power drawn from the main grid and forcing the MG to accurately follow this value by controlling a battery energy storage system. A mixed integer linear programming algorithm determines this reference value considering a time-of-use tariff and short-term forecasting of generation and consumption. A real-time predictive controller is used to control the battery energy storage system to follow this reference value. The results obtained show the capability of the proposed EMS to lower the daily operating costs for the MG customers. Experimental studies on a laboratory-based MG have been implemented to demonstrate that the proposed EMS can be implemented in a realistic environment

Hierarchical energy management system for controlling distributed energy resources in a community microgrid

Community Energy Systems (CES) can be used to unlock the potential of Distributed Energy Resources (DERs), maximize the local consumption of Renewable Energy Resources (RES) at the lowest level of electricity grid, and offer collective benefits to the end-users involved. If different electricity producers and consumers (prosumers) are connected to form a CES, the economic behaviour of the system needs to be fully understood. Therefore, a high priority in this important area is the development of a novel design procedure which allows the comprehensive and analytical investigation of the CES using integrating control, management strategies, optimal planning and scheduling and sizing procedures. This thesis presents novel centralized and decentralized hierarchical Community Energy Management Systems (CEMSs) which facilitate energy trading between prosumers in the CES by coordinating the operation of energy resources such as distributed or centralized battery energy storage and shiftable home appliances (located in each house) to achieve a further reduction in the daily household energy costs for each house, compared to being operated individually (i.e. not a part of the CES). The hierarchical CEMS represents an optimization-based real-time interactive algorithm which uses a combination of a Peer-to-Peer (P2P) energy trading scheme and a hierarchical optimization and control framework. This hierarchical CEMS reduces energy costs for end-users, maximizes self-consumption of locally generated energy, reduces the dependency of the CES on the main electrical grid, and reshapes the consumption profile of the CES to reduce peak consumption, while taking into account the battery degradation costs and the use of Demand Side Management (DSM) techniques. The novel structure of the hierarchical CEMS enables the algorithm to deal with frequent changes in the system using a short sample time. Detailed analysis of the performance of the household energy system using a real historic data of several UK households was performed to compare between end-users acting individually or as members of a CES. The performance of the household energy system is also assessed using different factors such as the overnight charging level, forecasting uncertainty, control sample time and tariff policies. Finally, a novel sizing methodology (in terms of energy and power rating) for a Community Battery Energy Storage System (CBESS) to provide Community Bill Management service plus addition ancillary services for the electricity/energy markets is presented. This includes an economic study to investigate if addition revenue could be obtained if the CBESS is used to provide more than one service. The results show the importance of participation in energy trading systems and the advantages of being a member of a CES, the need for using a centralized or a decentralized CEMS in coordination with energy trading systems to tackle the technical problems that may arise, and the importance of participation of the CES in the electricity market to achieve an appropriate return on investment

Performance Assessment of an Energy Management System for a Home Microgrid with PV Generation

Home energy management systems (HEMS) are a key technology for managing future electricity distribution systems as they can shift household electricity usage away from peak consumption times and can reduce the amount of local generation penetrating into the wider distribution system. In doing this they can also provide significant cost savings to domestic electricity users. This paper studies a HEMS which minimizes the daily energy costs, reduces energy lost to the utility, and improves photovoltaic (PV) self-consumption by controlling a home battery storage system (HBSS). The study assesses factors such as the overnight charging level, forecasting uncertainty, control sample time and tariff policy. Two management strategies have been used to control the HBSS; (1) a HEMS based on a real-time controller (RTC) and (2) a HEMS based on a model predictive controller (MPC). Several methods have been developed for home demand energy forecasting and PV generation forecasting and their impact on the HEMS is assessed. The influence of changing the battery’s capacity and the PV system size on the energy costs and the lost energy are also evaluated. A significant reduction in energy costs and energy lost to the utility can be achieved by combining a suitable overnight charging level, an appropriate sample time, and an accurate forecasting tool. The HEMS has been implemented on an experimental house emulation system to demonstrate it can operate in real-time

Energy management system for hybrid PV-wind-battery microgrid using convex programming, model predictive and rolling horizon predictive control with experimental validation

The integration of energy storage technologies with renewable energy systems can significantly reduce the operating costs for microgrids (MG) in future electricity networks. This paper presents a novel energy management system (EMS) which can minimize the daily operating cost of a MG and maximize the self-consumption of the RES by determining the best setting for a central battery energy storage system (BESS) based on a defined cost function. This EMS has a two-layer structure. In the upper layer, a Convex Optimization Technique is used to solve the optimization problem and to determine the reference values for the power that should be drawn by the MG from the main grid using a 15?min sample time. The reference values are then fed to a lower control layer, which uses a 1?min sample time, to determine the settings for the BESS which then ensures that the MG accurately follows these references. This lower control layer uses a Rolling Horizon Predictive Controller and Model Predictive Controllers to achieve its target. Experimental studies using a laboratory-based MG are implemented to demonstrate the capability of the proposed EMS

A hierarchical two-stage energy management for a home microgrid using model predictive and real-time controllers

This paper presents a hierarchical two-layer home energy management system to reduce daily household energy costs and maximize photovoltaic self-consumption. The upper layer comprises a model predictive controller which optimizes household energy usage using a mixed-integer linear programming optimization; the lower layer comprises a rule-based real-time controller, to determine the optimal power settings of the home battery storage system. The optimization process also includes load shifting and battery degradation costs. The upper layer determines the operating schedule for shiftable domestic appliances and the profile for energy storage for the next 24 h. This profile is then passed to the lower energy management layer, which compensates for the effects of forecast uncertainties and sample time resolution. The effectiveness of the proposed home energy management system is demonstrated by comparing its performance with a single-layer management system. For the same battery size, using the hierarchical two-layer home energy management system can achieve annual household energy payment reduction of 27.8% and photovoltaic self-consumption of 91.1% compared to using a single layer home energy management system. The results show the capability of the hierarchical home energy management system to reduce household utility bills and maximize photovoltaic self-consumption. Experimental studies on a laboratory-based house emulation rig demonstrate the feasibility of the proposed home energy management system

Far-Lateral Cervical Approach as a Minimally Invasive Technique for Excision of Upper Cervical Anterolateral and Anterior Meningiomas and Dumbbell Schwannomas: Technical Report and Case Series

Objective To demonstrate the details of the far-lateral approach (FLA) as a minimally invasive technique for the excision of the upper cervical anterolateral and anterior meningiomas and dumbbell schwannomas, and to assess the clinical and radiological outcomes. Methods In this technical report and case series we report the FLA technique and patients who underwent the FLA for C1-C4 anterolateral and anterior meningiomas and dumbbell schwannomas between June 2007 and June 2020. All patients’ relative preoperative demographic, clinical, radiographic, operative, histopathological, and perioperative complications and follow-up clinical and radiographic data were reported. Results A total of 19 patients including 12 females and 7 males with a mean age 56.7±17.6 years and mean duration of symptoms 12.8±12.3 months were reported. 9 patients with anterolateral meningiomas, 5 with anterior meningiomas, and 5 with dumbbell schwannomas underwent uneventful FLA procedures. Gross total resection of tumors was reported in 17 patients (89.5%). Preoperative JOA score was normal in ten, grade-I in five, and grade-II in 4 patients, while at the last follow-up it improved to normal in seventeen and grade-I in two patients. Reported postoperative JOA scores at 6 months and at the last follow-up showed that all patients improved at least one grade on JOA scores. There was CSF leak in three patients and superficial wound infection in one. Conclusion Our results advocate the far-lateral cervical approach as a minimally invasive technique in the resection of the upper cervical anterolateral and anterior meningiomas and dumbbell schwannomas as a safe and effective technique

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries