Energy Not Supplied Research Articles

Coexistence of nuclear and renewables in the V4 electricity system: Friends or enemies?

The paper examines the interactions between nuclear and variable renewable generation capacities (vRES) under various assumptions in the broader V4 region. Four exploratory scenarios are analysed with high and low penetration levels of vRES and nuclear applying electricity dispatch and unit commitment models. The assessment quantifies the impacts of the joint evolution of these technologies, measuring the effect on utilisation rates, wholesale prices, market values of vRES, energy not supplied (ENS) and the changing production and trading patterns in the projected 2035 electricity system. The results are indicative of a ‘double competition’ between (i) nuclear and vRES technologies within the merit order and (ii) between the NPPs in the region. If the ambitious V4 nuclear plans are indeed execute, NPPs will compete for limited export opportunities during times of high vRES production periods. Thus, coordination of long term energy policies within the V4 region is critical to manage nuclear and vRES developments and trade patterns with the aim of improving flexibility and security of supply to mitigate the negative economic impact on the electricity system.

Optimal design of hybrid DG systems for microgrid reliability enhancement

While the concept of microgrids and renewable energy systems is not entirely new, these integrated technologies have become a special topic of interest for researchers, utility providers and governments. Many challenges must be overcome to achieve better integration of renewable sources into the energy framework. This study presents some viable possibilities for the utilisation of a hybridised microgrid system. The hybridisation is achieved by an efficient design approach for the enhancement of both load and system reliability indices through the intelligent placement and sizing of hybrid distributed generation (DG) systems. Real-time models of solar photovoltaics, wind turbines, batteries and thermal DGs are presented and implemented. Also, network component failures are stochastically modelled via Monte Carlo simulations, and a general tie-set algorithm using an adapted breadth-first search is proposed. Moreover, mixed-integer multi-objective particle swarm optimisation is employed, giving a four-dimensional Pareto solution that is attained by optimising four reliability-related objectives, namely system average interruption frequency index, System Average Interruption Duration Index (SAIDI), Energy Not Supplied (ENS) and total cost.

Optimal Scheduling of Residential Electricity Demand Based on the Power Management of Hybrid Energy Resources

Abstract The present study sought to address the scheduling of the grid-connected hybrid energy resources under uncertainty of renewable sources, and load in the residential sector. After introducing hybrid resources, scheduling model was implemented through a power management algorithm in an attempt to optimize resource cost, emissions, and energy not supplied (ENS). The stated problem consists of two decision-making layers with different weight coefficients based on the prioritization of each objective function. The proposed algorithm is selected for energy optimal management based on technical constraints of the dispatchable and non-dispatchable resources, uncertainty parameters and day ahead real time pricing (RTP). Furthermore, the impact of demand response programs (DRP) on the given algorithm was investigated using load shedding and load shifting techniques. Finally, the results obtained led to the optimization of the functions in all decision-making layers with different modes of operation.

Optimal integration of renewable energy sources, diesel generators, and demand response program from pollution, financial, and reliability viewpoints: A multi-objective approach

In electric power systems , operators should account for the optimal operation of generation units to accommodate an efficient system with cleaner productions. In this paper, annual simultaneous planning and scheduling of generation resources are considered to determine the optimal capacity and type of the generation resources for microgrids (MGs). In the proposed approach, renewable energy sources (RESs), including wind turbines (WT) and photovoltaic systems (PVs), are incorporated in addition to diesel generators in each bus of the MG. The power loss of the MG is calculated by applying the Kron’s loss formula. Three different categories of loads are considered. The impact of consumer’s role on the performance of the demand response program (DRP) is also analyzed. Because of the stochastic nature of RESs, which influences the reliability, the impact of DRP on the energy not supplied (ENS) is studied. The proposed multi-objective model includes several conflicting objective functions including ENS, pollution, DRP, and operational costs. This model is solved by the ε-constraints method and optimized employing the exchange market algorithm (EMA). Simulation results highlight the impact of the generation resources’ types on the cost of operation, pollution, reliability, and power loss in the MG. The proposed approach will result in a system with cleaner production and improved financial condition. • A multi-objective model for investigating renewables integration is proposed. • Pollution, financial, and reliability issues are supposed as the objectives. • PVs, wind turbines, and diesel generators provide the demand of the microgrid. • Stochastic nature of renewables and demand response program are considered. • The optimal combination of the renewables and nonrenewables is obtained.

Allocation of Renewables, Switches, and Relays Considering Relays Actual Operation Time

System reliability enhancement of distributed generations (DGs) without aid of fast switches (FSs) is not possible. FSs are responsible for disconnecting unfaulted islands from faulty network to reduce energy not supplied (ENS). Directional overcurrent relays (DOCRs) are employed to send trip signals to FSs. In most of the DGs optimal allocation studies, the switches are assumed to operate correctly. This assumption might not be correct due to the delayed operation of the DOCRs. Few studies offer restricting minimum allowable size of DGs or allowable location of DOCRs to guarantee correct operation of DOCRs. These additional constraints limit the search space which causes nonoptimal answers. Here, the impact of actual operation of DOCRs is considered directly in calculation of ENS and hence in the final objective function. Also, a modified formulation for ENS in order to include cases where DGs capacity is not sufficient for supplying island loads is developed. Simultaneous allocation of DGs, FSs, and DOCRs to minimize cost of energy losses, ENS, and environmental costs are proposed. A multistate model that combines all possible operating conditions of DGs and load levels is utilized. Different cases are simulated in 33-bus, 69-bus, and 118-bus systems to evaluate the proposed method.

Optimal fuzzy switch placement to increase automation level of electric distribution network considering asset management principles

Since distribution sector is inherent into high amount of failures, distribution companies (DISCOs) are responsible of attaining an acceptable value for the reliability indices and otherwise they will face up to complaints. So they are usually obligated by regulators to invest on reliability improvement of network. But this investment on reliability is usually from the DISCO’s viewpoint and is also irrespective of customer satisfaction level. In other words, customers are not at the same level of sensitivity to interruptions but DISCO improves the reliability of network without considering the differences in importance degree of loads and their level of reliability requirement. On the other hand DISCOs attempt to reduce their investment costs as much as possible. This paper introduces a novel approach in the field of joint switch placement that can reduce the switch cost from the perspective of asset management policies. To this end, two switch placement plannings in different types of strategies are performed to compare their results. Firstly as witch placement is performed based on reducing the total energy not supplied (ENS) of the system. Then by revising the strategy, a fuzzy switch placement is performed from the DISCO’s point of view which just considers the total ENS of load points most sensitive to interruptions known as important or critical loads. Furthermore, by meeting the related constraints, the reliability of low sensitive customers is disregarded. This is a load importance based planning which can result in switch cost reduction relative to the amount achieved in previous strategy and implies the management of risks associated with reliability and respective constraint. Fuzzy method and new switching mechanism in fuzzy environment of network are implemented to modeling and controlling the risks associated to ENS of critical loads and also the ENS of system.

Impact of aging and performance degradation on the operational costs of distributed generation systems

Renewable and conventional generators in distributed generation system (DGS) are affected by aging and degradation progressively decreases their efficiency and nameplate capacity. This fact is, however, often omitted in the adequacy calculations of the energy not supplied (ENS) and O&M costs (Co) for DGS done via optimal power flow (OPF), leading to the underestimation of unreliability and costs. Moreover, these estimates are hard to compute due to the uncertain aging process. To overcome this limitation, we show that the degradation paths for generators of the same type are intrinsically variable, even for similar operating environments, and capture this by Wiener degradation process with unit-to-unit variability, whose parameters are estimated using the EM algorithm. The Wiener degradation model and the cross-correlation of Co and efficiency are validated using data from the Electric Utility Annual Report of the US Federal Energy Regulatory Commission. Monte Carlo Simulation and OPF are integrated to generate multiple degradation paths and variable operational conditions, and, ultimately, to evaluate the ENS and Co. The application to an empirical dataset and the IEEE 13 node test feeder shows that generator degradation has an increasing influence on the DGS and causes around 8% and 12% increments in ENS and Co.

Multi-objective optimization model for optimal reconfiguration of distribution networks with demand response services

Distribution networks are energy sectors that usually deal with residential energy consumers whose expectations associated with voltage quality and other relevant issues should be taken into account. There are various factors that might threaten reliable/economic performance of the distribution networks. In this paper, optimal reconfiguration of distribution systems is studied under a multi-objective optimization model to enhance reliability and minimize power losses of distribution networks. The developed multi-objective model is solved with epsilon-constrained (EPC) method and the trade-off conditions are achieved with min-max fuzzy satisfying (MMFF) technique. Demand response services (DRSs) including time-of-use (TOU) mechanism are proposed to make the energy consumers capable of revising their energy usage pattern. The proposed framework is investigated regarding the IEEE 33-bus test system in general algebraic modeling systems (GAMS) and the results validating the effectiveness of the proposed model are presented and compared. According to the obtained results, after reconfiguring the distribution system under the proposed model, loss of power in the system is reduced and reliability of a system associated with energy not supplied (ENS) index is improved. After proposing DRS and having the consumers motivated to change their consumption pattern, the obtained values for the mentioned targets are more suitable.

Fuzzy multi-objective placement of renewable energy sources in distribution system with objective of loss reduction and reliability improvement using a novel hybrid method

One of methods for loss reduction and reliability improvement of radial distribution system is using of renewable energy generation. In this paper, a new optimal placement and sizing of renewable energy sources based on photovoltaic panels (PVs) and wind turbines (WTs) in the distribution network is presented with the objective of loss reduction and reliability improvement based on energy not-supplied (ENS). A multi-objective evolutionary algorithm based on fuzzy decision-making method, called the Multi-Objective Hybrid Teaching–Learning Based Optimization-Grey Wolf Optimizer (MOHTLBOGWO) is proposed to solve the optimization problem. The proposed hybrid method has a high convergence speed and not trapped at all in local optimal. The proposed method is implemented in the form of single-objective and multi-objective on 33 and 69 bus IEEE radial distribution networks. The simulation results clear that the multi-objective optimization is a more precise approach to network utilization taking into account all objective indices than the single objective method. The results show that the proposed method has better convergence speed and less convergence tolerance in achieving to best solution in comparison with TLBO and GWO methods in loss reduction, reliability improvement and increasing the net saving and also in comparison with last studies. Moreover, the results show that dispersion of the size and location of distributed renewable generation leads to a further reduction in losses and a better improvement of the reliability criterion.

A Short-Term Energy Management of Microgrids Considering Renewable Energy Resources, Micro-Compressed Air Energy Storage and DRPs

The extensive penetration of renewable resources and utilizing advanced energy management techniques as well as enabling demand response programs (DRPs), especially in microgrids and active distribution networks, has had an impressive impact on the operation of such grids. Thus, with respect to the active and influential role of distribution grids in the restructured environment and the objective of achieving optimal operation of microgrids, energy management in this new environment requires more comprehensive analytic studies and rigorous researches. In this regard, the present research proposes a new strategy of optimal energy management and the subsequent day-ahead scheduling of a microgrid in the presence of micro compressed air energy storage (MCAES) and considering the uncertainties of renewable energy resources. The minimization of operation costs of energy storage facilities, environmental emission, the costs corresponded with the energy not supplied (ENS) and excess generation capacity are the main objectives of this study while the load satisfaction constraints are imposed. The technical constraints of distributed generation resources and energy storage facilities are imposed on this optimization problem. Besides, the execution of demand-side management programs is modelled to flatten the demand curve and to close the operational condition on the most optimum point. Employing the teaching-learning-based optimization (TLBO) algorithm, the proposed method is simulated for a test microgrid. The simulation’s results show that the utilization of MCAES facilities and executing DRPs have been concluded to the mitigation of generation cost, alleviation of emission, and reduction of ENS and excess generation capacity of the microgrid.

Fault Effect Analysis Based on Elements Loadability to Evaluate Reliability in Power Systems

This paper presents a fault effect analysis (FEA) based on element loadability as a complement tool to evaluate reliability and identify vulnerable points in power systems. The steps of the method are the calculation of overloaded elements after contingencies, determination of the FEA based on element loadability, selection of critical overloaded elements, and evaluation of system reliability by considering the energy not supplied (ENS). A simplified transmission and distribution (T and D) power system with 30 nodes was used as study case for the method. The results show that the loadability percentage of transformers and power lines are calculated successfully for the N-1 contingency analysis performed in the network and the method selects the elements with higher loadability values, evaluating the impact that each critical contingency generates on the power system reliability. The method can be used to prioritize remedial actions for critical elements on power systems.

A new coordinated design of sectionalizing scheme and load restoration process considering reliability of transmission system

One of the most important processes during load restoration is to use a suitable sectionalizing scheme to enhance the stability, especially when two islands are synchronized. In this regard, this paper provides a new scheme for the coordination of sectionalizing and load restoration to enhance the power system performance before and during the synchronization of islands. In doing so, there will be contradictory objective functions including maximizing the quality of sectionalizing process, minimizing the maximum standing phase angle (SPA) between islands, and minimizing the sum of energy not supplied (ENS) with unavailable energy capability (UEC) between all N-1 contingencies. Therefore, a multi-objective problem is defined as a mixed integer non-linear problem (MINLP). Also, a reliability-based index is defined to determine the quality of each island. Then, the θ-based water cycle algorithm (θ-WCA) is used to obtain the best Pareto optimal set. Two IEEE 39-bus and 118-bus power systems are used for validating the proposed method. The simulation results imply that the system can benefit from this scheme not only to have the good quality of sectionalizing, but also to enhance the power system performance during load restoration and the synchronization of islands.

Transactive energy integration in future smart rural network electrification

Abstract The electrification of current networks is developing to modernize and have a complete smart structure. Rural network electrification as a special part of the power grid will play a vital role in the future smart grid. However, energy management and balancing the supply and demand in rural area have created challenges for the distribution companies. In response to the mentioned challenges, the transactive energy (TE) as a new market based technology is applied to integrate the nine villages and manage the energy exchanging among them optimally. In order to evaluate the effects of TE technology on the rural network integration, three models are considered for analysis of the energy cost of each village and energy not supplied (ENS) in the presence of the renewable energy resources. First model assumed that none of the villages is equipped with renewable energy resources and all their electricity demand is fully provided by the power grid. While the renewable and non-renewable energy resources are considered for two other models. In the second model (individual mode), the villages cannot exchange energy with each other (local transaction market) and they can only connect to the power grid, but in the third model (integrated mode) the villages have an interconnected situation and may exchange energy with each other and grid under the TE management. Simulation results for the nine villages located in India are presented and evaluated. The results investigation indicated that the cost of energy and ENS are improved in the third model in comparison to other models.

Chance constrained simultaneous optimization of substations, feeders, renewable and non-renewable distributed generations in distribution network

The penetration of distributed generators (DGs) is continually increasing in the power sector due to its ability in enhancing technical specifications as well as providing a promising future for power generation in electric networks. The aforementioned objectives will be realized if DG units are allocated optimally and coordinately simultaneous with distribution network expansion planning. On the other hand, given the stochastic nature of renewable generation and severe fluctuations of load consumption and electricity price, the DGs planning problem should be accomplished under uncertainties. To address these issues, this paper proposes a novel joint chance constrained programming (JCCP) method to fulfill an acceptable level of constraint feasibility for optimal simultaneous expansion planning of HV/MV substations and multiple-DG units along with robust MV feeder routing problem. Our design objective is to determine the optimal site and size of sub-transmission substations and various DG units associated with optimally construction of network by implementing the feeder routing problem with aim to minimize the investment costs, energy not supplied (ENS) cost and energy purchasing cost from upstream network. The diverse objectives are mathematically formulated as an MINLP model and converted into a single-objective function through weighted sum method and subsequently has been minimized by adaptive genetic algorithm. Furthermore, the Taguchi method is utilized in order to furnish an efficient algorithm that can find a satisfactory solution. Finally, the effectiveness of the proposed method is investigated by applying it on the 54-bus distribution network and the obtained results are duly drawn and discussed.

Network Reliability Analysis as a Tool to Guide Investment Decisions in Distributed Generation

Distributed Generation (DG) in any quantity is relevant to supplement the available energy capacity based on various locations, that is, whether a site specific or non-site specific energy technology. The evacuation infrastructure that delivers power to the distribution grid is designed with appropriate capacity in terms of size and length. The evacuation lines and distribution network however behave differently as they possess inherent characteristics and are exposed to varying external conditions. It is thus feasible to expect that these networks behave stochastically due to fault conditions and variable loads that destabilize the system. This in essence impacts on the availability of the evacuation infrastructure and consequently on the amount of energy delivered to the grid from the DG stations. Reliability of the evacuation point of a DG is however not a common or prioritized criteria in the decision process that guides investment in DG. This paper reviews a planned solar based DG plant in Uganda. Over the last couple of years, Uganda has seen a significant increase in the penetration levels of DG. With a network that is predominantly radial and experiences low reliability levels, one would thus expect reliability analysis to feature significantly in the assessment of the proposed DG plants. This is however not the case. This paper, uses reliability analysis to assess the impact of different evacuation options of the proposed DG plant on its dispatch levels. The evacuation options were selected based on infrastructure options in other locations with similar solar irradiances as the planned DG location. Outage data were collected and analyzed using the chi square method. It was found to be variable and fitting to different Probability Distribution Functions (PDF). Using stochastic methods, a model that incorporates the PDFs was developed to compute the reliability indices. These were assessed using chi square and found to be variable and fitting different PDFs as well. The viability of the project is reviewed based on Energy Not Supplied (ENS) and the anticipated project payback periods for any considered evacuation line. The results of the study are also reviewed for the benefit of other stakeholders like the customers, the utility and the regulatory body.

A heuristic multi-objective multi-criteria demand response planning in a system with high penetration of wind power generators

Integration of wind energy and other renewable energy resources in electrical systems create some challenges due to their uncertain and variable characteristics. In the quest for more flexibility of the electric systems, combination of these endogenous and renewable resources in accordance with strategies of Demand Response (DR) allows an increment/improvement of the demand potential, as well as a more secure, robust, sustainable and economically advantageous operation. This paper proposes a new model for integration of wind power and DR, thus optimizing supply and demand side operations through a price rule Time of Use (TOU), or incentive with Emergency DR Program (EDRP), as well as combining TOU and EDRP together. The problem is modelled using a stochastic Heuristic Multi-Objective Multi-Criteria Decision Making (HMM) method which aims to minimize operation costs and environmental emissions simultaneously, ensuring the security constraints through two-stage stochastic programming, considering various techno-economic indices such as load factor, electricity market prices, Energy Not Supplied (ENS) and Share Weighted Average Lerner Index (SWALI). Comprehensive numerical results indicate that the proposed model is entirely efficient in DR planning and power system operation.

Stochastic multi-objective model for optimal energy exchange optimization of networked microgrids with presence of renewable generation under risk-based strategies

The inherent volatility and unpredictable nature of renewable generations and load demand pose considerable challenges for energy exchange optimization of microgrids (MG). To address these challenges, this paper proposes a new risk-based multi-objective energy exchange optimization for networked MGs from economic and reliability standpoints under load consumption and renewable power generation uncertainties. In so doing, three various risk-based strategies are distinguished by using conditional value at risk (CVaR) approach. The proposed model is specified as a two-distinct objective function. The first function minimizes the operation and maintenance costs, cost of power transaction between upstream network and MGs as well as power loss cost, whereas the second function minimizes the energy not supplied (ENS) value. Furthermore, the stochastic scenario-based approach is incorporated into the approach in order to handle the uncertainty. Also, Kantorovich distance scenario reduction method has been implemented to reduce the computational burden. Finally, non-dominated sorting genetic algorithm (NSGAII) is applied to minimize the objective functions simultaneously and the best solution is extracted by fuzzy satisfying method with respect to risk-based strategies. To indicate the performance of the proposed model, it is performed on the modified IEEE 33-bus distribution system and the obtained results show that the presented approach can be considered as an efficient tool for optimal energy exchange optimization of MGs.

Multi‐objective optimization model for distribution network reconfiguration in the presence of distributed generations

Summary With increasing annual energy demand and significant growth in the use of distributed generation (DG), distribution companies are exploring ways to improve the technical specifications of the distribution networks such as power losses, customer interruption costs, and voltage stability and on the other hand to increase the penetration level of DGs. In this paper, a multi-objective optimization model is proposed for distribution network reconfiguration (DNR) to decrease the total costs of energy losses and energy not supplied (ENS) and increase the voltage stability and DG penetration level. Seasonal hourly load curves are applied, and best configuration is determined for each loading condition. Uncertainties regarding the load variations and volatile power production of wind turbine are modeled using a 2-point estimate method, and a benefit-cost analysis is used to assess the effects of depreciation cost in DNR problem. The proposed model is applied on 2 typical test systems.

Optimal recloser deployment to leverage self-healing: a techno-economic robustness assessment

This study presents a techno-economic robustness assessment over coordinated protection schemes using reclosers to leverage self-healing in distribution networks. A methodology to assess the optimal deployment of reclosers in overhead distribution networks is used to define the number and the strategic location of reclosers, in order to achieve the maximisation of the reliability improvement and the investment cost effectiveness. The methodology comprises a technical assessment over the service restoration feasibility through fault detection, isolation and restoration, and an economic assessment through a cost–benefit analysis over the equipment's life cycle. A real Portuguese distribution network is used to perform a case study comprising two scenarios of operation under contingency, a base case scenario and an additional scenario considering the presence of renewable energy sources – wind power plants. The advanced sensitivity analysis focuses on the evaluation of the optimal solution robustness to key parameters – technical and economic – such as the value of energy not supplied (ENS), the load growth rate and the value of the wind curtailment.

Reliability Effects of Maintenance on TNEP Considering Preventive and Corrective Repairs

The aim of this paper is to investigate maintenance effects on system reliability and transmission network expansion planning considering line loading and repairs. For this purpose, the maintenance cost is formulated taking into account transient and permanent forced outage rates, as well as durations of planned and forced outage. Also, transmission reliability is modeled considering load shedding (LS) and energy not supplied (ENS) criteria. LS index is calculated for transient forced outages and ENS criterion is computed for permanent forced outages, and planned outage rates and durations. Furthermore, the effects of line loadings on transmission system reliability are formulated through the transient and permanent forced outage rates. The proposed model was tested on the IEEE 24-bus and 30-bus test systems, and the results are discussed.