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We propose a stochastic mixed integer optimization model to optimally size a solar power system and its battery storage for residential and nonresidential customers of electric power. The objective function of the model is to minimize the total cost associated with solar power system investments and the grid provided electric power over a planning horizon. We consider the uncertainty associated with solar radiation, load, and electricity price in the form of probabilistic scenarios. The model can be used with different grid pricing programs and under no net metering or net metering programs, respectively. A numerical example and its parametric analyses are used to demonstrate the efficacy of the model and develop some insights into optimal sizing of a battery storage enabled solar system. The analyses show the size of the solar system is influenced by the labor cost and the load size whereas the size of the battery storage is sensitive to the load size and the battery cost. Moreover, we find the optimal number of solar panels/batteries is larger under the net metering program than under no net metering program. [ABSTRACT FROM AUTHOR]

In this century, problems such as the scarcity of fossil fuel resources and related environmental contamination have led to the emergence of new energy systems based on renewable energy resources. In this paper, an optimal planning approach is proposed based on 100% renewable energy system (RES) for a residential house. In respect to renewable resources potential in the site location and electrical demand, the best combination of resources is chosen based on minimum energy supply cost and maximum reliability. Furthermore, different scenarios are suggested by considering different levels of capacity shortage (CSH) and unmet electricity load (UEL) percentage. As a case study, the real electricity consumption data for a single family household is considered in Hesarak, Tehran, Iran. The final optimal solution for this 100% RES with the objective function of cost minimization and reliability constraint include 4 kW PV, 2 kW wind turbine, 4 kW converter and 6 battery strings. This scenario with CSH of 1.1% and UEL of 0.9% has the net present cost of 20,527 $ that while having low cost, the reliability of this system is also good compared to other scenarios. [ABSTRACT FROM AUTHOR]

The cooperation of multiple networked microgrids (MGs) can alleviate the mismatch problem between distributed generation and demand and reduce the overall cost of the power system. Energy management with direct energy exchange among MGs is a promising approach for improving energy efficiency. However, existing methods on microgrid cooperation usually overlook the underlying distribution network with operating constraints (e.g., voltage tolerance and power flow constraints). Hence the results may not be applicable to actual systems. This paper studies the energy management problem of multiple MGs that are interconnected by both the direct current (DC) energy exchange network and the alternating current (AC) traditional distribution networks. In our problem, each MG is equipped with renewable energy generators as well as distributed storage devices. In order to handle the non-convex power flow constraints, we exploit the recent results of the exact optimal power flow (OPF) relaxation method which can equivalently transform the original non-convex problem into a second-order cone programming problem and efficiently determine the optimal solution successfully. The objective of our problem is to minimize the overall energy cost in a distribution network consisting of multiple MGs, with the practical operating constraints (e.g., power balance and the battery’s operational constraints) explicitly incorporated. Considering the privacy and scalability, we propose a distributed algorithm with convergence assurance based on the alternating direction method of multipliers (ADMM). We also implement our method based on the model predictive control (MPC) approach in order to handle the forecasting errors of the renewable energy generation. Simulations are made for different MG exchange topologies on three radial distribution network testbeds. Numerical results demonstrate that certain topologies are more favorable than others, and the cooperation strategy for the energy exchange is significantly affected by the MGs’ locations in the distribution network. [ABSTRACT FROM AUTHOR]

In this paper, an approach for placement of voltage regulators (VRs) in traditional distribution systems by considering a local controller model is presented. The main aims of this paper are controlling the voltage level in its permitted range and decreasing the costs imposed to the distribution system companies, such as costs that stem from power losses, VRs’ investment and maintenance. Genetic algorithm (GA) has been used as a tool to determine the number, location and rated power of VRs. Since in traditional distribution systems, tap position determination of VRs is achieved by local controllers, local controller model is established to determine tap operations. A 70-bus distribution system is considered to prove the value of the presented approach. Effectiveness of the proposed approach and ineffectiveness and infeasibility of conventional approaches are presented in numerical studies. The presented approach allowed to eliminate voltage violation in all load conditions and a reduction of power losses of about 6% for the maximum load level. [ABSTRACT FROM AUTHOR]

The Midwest is the largest Brazilian agricultural region, far from major power production units. The availability of energy close to consumption, or distributed generation (DG), can reduce distribution costs and losses. We verified the possibility of using electric power from sugarcane biomass to irrigate corn crops through interviews with sugarcane companies and farmers. Direct commercialization of energy from sugarcane biomass for irrigation is not viable due to energy subsidies received by irrigating farmers. Most sugarcane energy is sold into the national network, without considering its origin. DG has potential, but its development involves the increase of rural energy consumption, such as for irrigation. [ABSTRACT FROM AUTHOR]

The management of renewable and distributed energy resources is changing the way in which electric distribution systems are being operated. Many companies commonly create several custom system solutions to manage power distribution. The functions provided by these systems are network visualization, state estimation, system control and data acquisition, among others. Despite their relevance, many mid-size or small distribution companies have problems to install one of these solutions because they are either very expensive, incompatible, or limited by difficulties in data exchange. As a possible solution to this problem, this paper presents the development of a Distribution Management System (DMS) based on open source technology. This system combines four main components: OpenDSS-framework, ActiveMQ-broker, applications for visualizing and editing electrical network, and Common Information Model (CIM). The tool is part of Tenergia R&D project 1 This work is financed by R&D projects FITS 2013 – UREE10 – FONARSEC – Ministry of Science, Technology and Productive Innovation of Argentina, and PIDDEF 36/14 – Ministry of Defense of Argentina. 1 and has a collection of applications designed to monitor and control the distribution networks located in Argentina. [ABSTRACT FROM AUTHOR]

This article examines the use of publicly-available financial and operating data to benchmark distribution performance of U.S. IOUs. The authors describe a methodology addressing characteristics beyond IOU control commonly held to impact performance. Econometric analyses refute many commonly-held beliefs about the influence of various characteristics on various performance measures, and support other beliefs only weakly. The authors then apply appropriate adjustments quantified through regression analyses to create a distribution business performance ranking of U.S. IOUs. [ABSTRACT FROM AUTHOR]

The policy of net metering allows operators of residential- and commercial solar PV systems to sell surplus electricity back to their utility at the going retail rate. This policy has recently been criticized on the grounds that it provides a subsidy for residential and commercial solar installations, a subsidy that is paid for by all ratepayers. In response, public utility commissions have begun to take up this regulatory issue. This paper presents a theoretical and empirical analysis of the effects of net metering restrictions. We examine the impact that overage tariffs (OT) will have on the size of future residential PV investments. Overage tariffs credit electricity generated by the solar system, but not consumed by the household and thus transferred back to the utility, at a rate below the going retail electricity rate. Our calculations focus on three representative locations in the states of California, Nevada and Hawaii. We find that if overage electricity is credited at some rate set at or above the levelized cost of electricity (LCOE), there would be sufficient incentives for investors in residential solar PV to continue in the current mode of solar rooftop installations, even though the profitability of these investments might be reduced substantially. At the same time, we find that the LCOE is a tipping point insofar as overage tariffs set below that level would have a sharply negative effect on the individual size and overall volume of new rooftop installations. [ABSTRACT FROM AUTHOR]

This study aims to develop a new approach for design and evaluation of renewable energy sources-based energy supply system using different energy demand scenarios and an optimization model. To achieve this goal, we first generate six scenarios by the combination of different supply and demand options. We then develop a new optimization model for identifying the optimal system configuration by minimizing the total energy cost subject to various constraints. We apply the proposed approach to the design problem of energy system of Jeju Island, Korea. We evaluate the different scenario-based systems of Jeju Island using economic metrics including net present cost, levelized cost of energy and annual energy expenses. The sensitivity of major process and economic parameters, and different meteorological data on the annual energy expenses is also discussed. As a result, the levelized energy cost was estimated to range from 0.53 to 1.34 $/kWh according the examined scenarios. We analyzed that the centralized energy production by the hybrid use of wind and solar energy is the most affordable system. We also identified the key cost driver in the distributed and centralized energy production systems; i.e., the capital cost of the wind turbine and the energy storage facilities, respectively. [ABSTRACT FROM AUTHOR]

Few studies have been reported for the small scale LNG regasification units in power generation systems although many applied LNG cold utilization systems exist for the large scale regasification plants. Impact of the LNG cold utilization system is investigated for the micro cogeneration systems thermoeconomically. An exergy-cost matrix is generated for the systems, which then compared to the conventional micro cogeneration systems. Ambient air temperature and compressor pressure ratio are analyzed. The required work for the LNG pump is found to be neglected in overall cycle analyses. In the cogeneration case, the exergetic and energetic efficiencies are roundly two and three times higher than the single power generation case. The exergoeconomic factor is roundly 26.8% less in the LNG cold utilized system while the relative cost is 75% higher in the conventional case. The LNG cold utilization increases the levelized product costs about 1.7 times at the pressure ratio of 3.64. A graphic-based single objective optimization is conducted by determining unit product cost and exergetic efficiency as the objectives. The maximum relative unit product cost difference is found at 308.15 K while the minimum relative exergetic efficiency difference is at 288.15 K. We suggest system improvements through the thermoeconomic evaluation. [ABSTRACT FROM AUTHOR]

Despite their importance for the proper operation of both care and non-care services, there are no models in the literature analysing the choice of the most suitable maintenance strategy to be applied in electric power distribution systems in Health Care Organizations. This paper presents an innovative multicriteria which contributes to ensuring the best quality of care for the patient. The model integrates the Measuring Attractiveness by a Categorical Based Evaluation Technique approach with Markov chains to obtain the expected mean availability for different electric power distribution systems. The result is a full classification of the combination of maintenance policies and actions (redundancy), to be applied in power distribution systems. The model gives, as the most suitable maintenance strategy, a combination of corrective, preventive and predictive maintenance (thermal analysis). It is the first model produced and applied to a variety of electric power distribution systems responsible for supplying electrical energy to critical areas of the Health Care Organizations, such as operating theatres, intensive care units, paediatrics, general lighting and other specific areas. The result obtained is compared with the maintenance policy currently used by the organization, and a plan for the implantation of the alternative given for the model is produced. [ABSTRACT FROM AUTHOR]

We introduce a MOO (multi-objective optimization) framework for the integration of renewable DG (distributed generation) into electric power networks. The framework searches for the optimal size and location of different DG technologies, taking into account uncertainties related to primary renewable resources availability, components failures, power demands and bulk-power supply. A non-sequential MCS-OPF (Monte Carlo simulation and optimal power flow) computational model is developed to emulate the network operation by generating random scenarios from the diverse sources of uncertainty, and assess the system performance in terms of CG (global cost). To measure uncertainty in the system performance, we introduce the DCVaR (conditional value-at-risk deviation) which, due to its axiomatic relation to the CVaR (conditional value-at-risk), allows the conjoint control of risk. A MOO strategy can, then, be adopted for the concurrent minimization of the ECG (expected global cost) and the associated deviation DCVaR ( CG ). In our work this is operatively implemented by a heuristic search engine based on differential evolution (MOO-DE). An example of application of the proposed framework is given with regards to the IEEE 30 bus test system, where in DCVaR is shown capable of enabling and controlling tradeoffs between optimal expected economic performance, uncertainty and risk. [ABSTRACT FROM AUTHOR]

An investigation of the link between electricity theft and power disruptions simultaneously provides a rare and contemporary cross-country assessment of electricity theft. The findings suggest that electricity theft in Haiti ranked first overall, and that extensive power theft has risen internationally since 1980. The direct cost of electricity disruptions ranged from US$0.43 to US$9.91 per kWh. [ABSTRACT FROM AUTHOR]