A new Risk-Managed planning of electric distribution network incorporating customer engagement and temporary solutions

The connection of renewable-based distributed generation (DG) in distribution networks has been increasing over the last few decades, which would result in increased network capacity to handle their uncertainties along with uncertainties associated with demand forecast. Temporary non-network solutions (NNSs) such as demand response (DR) and temporary energy storage system/DG are considered as promising options for handling these uncertainties at a lower cost than network alternatives. In order to manage and treat the risk associated with these uncertainties using NNSs, this paper presents a new risk-managed approach for multi-stage distribution expansion planning (MSDEP) at a lower cost. In this approach, the uncertainty of available DR is also taken into account. The philosophy of the proposed approach is to find the “optimal level of demand” for each year at which the network should be upgraded using network solutions while procuring temporary NNSs to supply the excess demand above this level. A recently developed forward-backward approach is fitted to solve the risk-managed MSDEP model presented here for real sized networks with a manageable computational cost. Simulation results of two case studies, IEEE 13-bus and a realistic 747-bus distribution network, illustrate the effectiveness of the proposed approach

Cost–benefit analysis of a virtual power plant including solar PV, flow battery, heat pump, and demand management: A Western Australian case study

Achieving the renewable energy integration target will require the extensive engagement of consumers and the private sector in investment and operation of renewable-based energy systems. Virtual power plants are an efficient way to implement this engagement. In this paper, the detailed costs and benefits of implementing a realistic virtual power plant (VPP) in Western Australia, comprising 67 dwellings, are calculated. The VPP is designed to integrate and coordinate rooftop solar photovoltaic panels (PV), vanadium redox flow batteries (VRFB), heat pump hot water systems (HWSs), and demand management mechanisms. An 810-kW rooftop solar PV system is designed and located using the HelioScope software. The charging and the discharging of a 700-kWh VRFB are scheduled for everyday use over a year using an optimization algorithm, to maximize the benefit of it for the VPP owners and for the residents. The use of heat pump HWSs provides a unique opportunity for the residents to save energy and reduce the total cost of electricity along with demand management on some appliances. The cost-and-benefit analysis shows that the cost of energy will be reduced by 24% per dwelling in the context of the VPP. Moreover, the internal rate of return for the VPP owner is at least 11% with a payback period of about 8.5 years, which is a promising financial outcome

The energy-water nexus: Renewable energy and water desalination

The essential connection between energy and water, also defined as the energy-water nexus, has been recognized by scientists and policy makers worldwide. Integrated solutions and policies that consider both energy and water aspects into future planning have been developing at a fast pace. In this paper, we review the state of the art of the energy-water nexus, with particular focus on the integration between renewable energy and desalination technologies. We also model the integration of reverse osmosis (RO) desalination and solar photovoltaics in an edge-of-grid coastal town in Western Australia. The current literature agrees on the sustainable use of renewable energy sources to improve the water-energy nexus in the context of water desalination. Although the integration of solar and wind energy with desalination technologies is a mature and well-proven solution at both small and large scales, the intermittency and fluctuating nature of wind and solar power still constitute the main technical challenge that has limited the diffusion of renewable energy powered desalination on a large scale. Several successful applications of renewable energy powered desalination in remote, off the grid, locations have tackled the issue of power intermittency by the use of batteries and diesel generators. Such systems often couple reverse osmosis desalination with solar photovoltaic energy. Large desalination plants have been successfully connected to wind farms and grid electricity to secure uninterrupted plant operations, thus meeting water targets in large-scale systems. Our review identifies a knowledge gap in the integration of decentralized energy systems, e.g. rooftop solar photovoltaic, with small scale RO desalination. Such configuration would benefit those regional towns that have historically suffered from weak and unreliable connections to the electricity grid, thus helping them secure both their energy and water requirements. The modelling exercise on a renewable energy powered RO plant in an edge-of-grid town in Western Australia has identified an operating strategy that maximizes the renewable energy fraction and secures the annual supply of water. The system involves operating the RO unit for six months of the year at a daily variable load and integrating solar energy with grid electricity. Careful evaluation of the RO performance under such operating conditions is necessary to ensure a safe and reliable water treatment process. A niche in the literature of the energy-water nexus has been identified in the integration of rooftop solar photovoltaic, grid electricity and desalination technologies applied in a regional context. A future study will consider the rollout of rooftop solar photovoltaic installations across the whole town, thus enabling the active engagement of the community by integrating the households’ energy demand response patterns to the operations of both rooftop photovoltaics and the desalination unit

The energy-water nexus: Renewable energy and water desalination

The essential connection between energy and water, also defined as the energy-water nexus, has been recognized by scientists and policy makers worldwide. Integrated solutions and policies that consider both energy and water aspects into future planning have been developing at a fast pace. In this paper, we review the state of the art of the energy-water nexus, with particular focus on the integration between renewable energy and desalination technologies. We also model the integration of reverse osmosis (RO) desalination and solar photovoltaics in an edge-of-grid coastal town in Western Australia. The current literature agrees on the sustainable use of renewable energy sources to improve the water-energy nexus in the context of water desalination. Although the integration of solar and wind energy with desalination technologies is a mature and well-proven solution at both small and large scales, the intermittency and fluctuating nature of wind and solar power still constitute the main technical challenge that has limited the diffusion of renewable energy powered desalination on a large scale. Several successful applications of renewable energy powered desalination in remote, off the grid, locations have tackled the issue of power intermittency by the use of batteries and diesel generators. Such systems often couple reverse osmosis desalination with solar photovoltaic energy. Large desalination plants have been successfully connected to wind farms and grid electricity to secure uninterrupted plant operations, thus meeting water targets in large-scale systems. Our review identifies a knowledge gap in the integration of decentralized energy systems, e.g. rooftop solar photovoltaic, with small scale RO desalination. Such configuration would benefit those regional towns that have historically suffered from weak and unreliable connections to the electricity grid, thus helping them secure both their energy and water requirements. The modelling exercise on a renewable energy powered RO plant in an edge-of-grid town in Western Australia has identified an operating strategy that maximizes the renewable energy fraction and secures the annual supply of water. The system involves operating the RO unit for six months of the year at a daily variable load and integrating solar energy with grid electricity. Careful evaluation of the RO performance under such operating conditions is necessary to ensure a safe and reliable water treatment process. A niche in the literature of the energy-water nexus has been identified in the integration of rooftop solar photovoltaic, grid electricity and desalination technologies applied in a regional context. A future study will consider the rollout of rooftop solar photovoltaic installations across the whole town, thus enabling the active engagement of the community by integrating the households’ energy demand response patterns to the operations of both rooftop photovoltaics and the desalination unit

An incentivized and optimized dynamic mechanism for demand response for managing voltage in distribution networks

The voltage regulation in distribution networks is one of the major obstacles when increasing the penetration of distributed generators (DGs) such as solar photovoltaics (PV), especially during cloud transients, causing potential stress on network voltage regulations. Residential demand response (DR) is one of the cost-effective solutions for voltage management in distribution networks. However, the main barriers of DR implementation are the complexities of controlling a large number and different types of residential loads, satisfying customers’ preferences and providing them fair incentives while identifying the optimum DR implementation locations and sizing as well as cooperating with the existing network equipment for the effective voltage management in the networks. A holistic and practical approach of DR implementation is missing in the literature. This study proposes a dynamic fair incentive mechanism using a multi-scheme load control algorithm for a large number of DR participants coordinating with the existing network equipment for managing voltage at medium voltage (MV) networks. The multi-scheme load control is comprised of short-interval (10-minute) and long-interval (2-hour) DR schemes. The dynamic incentive rates are optimized based on the energy contribution of DR participating consumers, their influence on the network voltage and total power loss improvement. The proposed method minimizes the DR implementation cost and size, fairly incentivizes the consumers participating in the DR and priorities their consumption preferences while reduces the network power losses and DGs’ reactive power contributions to effectively manage the voltage in the MV networks. An improved hybrid particle swarm optimization algorithm (IHPSO) is proposed for the load controller to provide fast convergence and robust optimization results. The proposed approach is comprehensively tested using the IEEE 33-bus and IEEE 69-bus networks with several scenarios considering a large number of DR participants coordinated with the DGs and on-load tap changer (OLTC) in the networks

Energy management strategy in dynamic distribution network reconfiguration considering renewable energy resources and storage

© 2010-2012 IEEE. Penetration of renewable energy sources (RESs) and electrical energy storage (EES) systems in distribution systems is increasing, and it is crucial to investigate their impact on systems' operation scheme, reliability, and security. In this paper, expected energy not supplied (EENS) and voltage stability index (VSI) of distribution networks are investigated in dynamic balanced and unbalanced distribution network reconfiguration, including RESs and EES systems. Furthermore, due to the high investment cost of the EES systems, the number of charge and discharge is limited, and the state-of-health constraint is included in the underlying problem to prolong the lifetime of these facilities. The optimal charging/discharging scheme for EES systems and optimal distribution network topology are presented in order to optimize the operational costs, and reliability and security indices simultaneously. The proposed strategy is applied to a large-scale 119-bus distribution test network in order to show the economic justification of the proposed approach

Detection of Greening in Potatoes using Image Processing Techniques

Abstract: Quality is one of the important factors in marketing of agricultural products. Grading machines have great importance in the quality inspection systems. Most of the current grading machines operate based on machine vision systems to detect blemishes and defects of products, where one image or more are taken for each individual object and the results of processing will decide the quality of the object. One of the major blemishes in potatoes is physiological skin greening, which has negative influence on human health. In this research, a simple machine vision algorithm has been developed in order to detect physiological skin greening of potato tubers rapidly and precisely. The experimental image acquisition setup was consisted of an image capturing box equipped with lighting system, a color CCD camera, and a capturing card. The data set consisted of 25 images of potatoes with physiological skin greening blemishes. Image pre-preprocessing has been carried out to modify the non-uniform distribution of background light intensity. Since potatoes have bright skin, the CCD was saturated in a small part of each image. These parts were eliminated from the images using a relation found between RGB and HSI spaces. The difference between red and green components of RGB space for green parts of potatoes was lower than that of other parts. Finally, the 1.02R -G relation was found to be suitable for detection of green parts of potato tubers. The average of error between actual green parts area and estimated green parts area for 25 images was

A Linear-based Model for Multi-Microgrid Energy Sharing- A Western Australia Case Study

This paper proposes a model for energy sharing of interconnected microgrids (MGs), mainly where some MGs are owned by an entity, such as the government, which is the case study in Western Australia (WA). In the proposed model, MGs are able to trade energy among themselves when some of them have surplus generation, and others have lack of generations to meet their demand; however, they are obliged to pay for the use of distribution network, called network charge, and the share of network loss due to this energy transaction. In doing so, the network loss is taken into account and calculated through a power flow. The possibility of energy trading with the main grid is also considered through the wholesale electricity market. Considering the uncertainty of Photovoltaic (PV) generation and load involved, the decision making to inject or import energy to/from the main grid as well as to trade between MGs is obtained through a bi-level linear optimization. In the upper level, the distribution network operator intends to manage the energy exchange between MGs and energy trading with upstream grid, while in the lower level, each MG attempt to minimize its operational cost relating to PV and energy storage system (ESS). Finally, the proposed method is applied to a real project in Western Australia

An Effective Approach for Locational Marginal Price Calculation at Distribution Level

This paper develops an effective approach for the locational marginal price calculation for local generations in an active distribution network containing different types of distributed generators (DGs). The proposed approach is based on encouraging private units to reduce power loss and greenhouse gas (GHG) emissions. To this end, firstly, the distribution system operator (DSO) surplus profit, obtained by the reduction of power loss and GHG gas emission due to the operation of private units in the network, is considered as a financial source for encouraging private units. Then, according to the contribution of each private DG, the locational marginal price is calculated. The proposed approach is an effective and incentive-based approach for DSO to retain control over private units to reduce power loss and GHG emissions. The simulation results on a modified 118-bus standard distribution test system demonstrate the efficiency of the proposed approach compared to the previous approaches

Aggregating energy supply and demand

Energy usage in general, and electricity usage in particular, are major concerns internationally due to the increased cost of providing energy supplies and the environmental impacts of electricity generation using carbon-based fuels. If a "systems" approach is taken to understanding energy issues then both supply and demand need to be considered holistically. This paper examines two research projects in the energy area with IT tools as key deliverables, one examining supply issues and the other studying demand side issues. The supply side project used hard engineering methods to build the models and software, while the demand side project used a social science approach. While the projects are distinct, there was an overlap in personnel. Comparing the knowledge extraction, model building, implementation and interface issues of these two deliverables identifies both interesting contrasts and commonalities