Loss Of Load Expectation Research Articles

Interval Reliability Evaluation of a Hybrid Energy Generation System With Energy Storage

To deal with the uncertainties of wind power and load residing in the power supply reliability model, an interval reliability evaluation method is proposed by combining the wind power generation and energy storage system (ESS). Firstly, the interval power supply reliability evaluation model, which belongs to an interval mixed integer program (IMIP), is established based on the interval variables. Secondly, the IMIP model is transformed into the deterministic optimization model under two extreme circumstances by utilizing the possibility degree theory of interval numbers. The maximum power supply probability, considering the wind power interval to meet the load demand interval, is sought by optimizing outputs of the ESS and generators, i.e., the upper boundary of the load shedding is the smallest. Finally, the states of wind turbines and generators are generated based on sequential Monte Carlo simulation, and the reliability of the hybrid energy generation system is evaluated by calculating the loss of load expectation, expected energy not supplied, and maximum power supply probability, which provides a basis for establishing interval optimal allocation model of energy storage. IEEE RTS-24 test system is utilized to verify the performance of the proposed method, and the model is solved by the CPLEX 12.7 solver. The simulation results demonstrate the effectiveness and applicability of the proposed method.

Well-Being Analysis Applied to the Study of Composite Systems Flexibility Considering Wind Energy Sources

This paper presents a methodology to evaluate, via well-being analysis, the flexibility of composite electrical systems, considering wind energy sources. By adding wind farms to different system bars, it is possible to assess which configuration provides the best adequacy and safety level for the system. The evaluation is based on probabilistic indices of Loss of Load Expectation (LOLE) and Expected Marginal State (EMS). Such indices are obtained by well-being analysis, combining deterministic criteria with probabilistic methods. Non-Sequential Monte Carlo Simulation is used to consider the probabilistic and intermittent characteristics of wind sources. The methodology is applied to the IEEE-RTS test system.

Renewable Energy Integration in Vietnam’s Power System: Generation Adequacy Assessment and Strategic Implications

Vietnam is widely considered as an energy-intensive economy. Renewable energy integration has been set as an important goal in the country’s revised Power Development Plan 7 (PDP7). This study first conducted a generation adequacy assessment using the basic probabilistic modeling approach to evaluate how the generation fleet, as foreseen in the PDP7, can meet the demand, despite the fast-changing renewable energy sources (RES) generation. The adequacy of the generation was measured using the Loss of Load Expectation (LOLE, expressed in hours) index. The study then conducted in-depth interviews with key stakeholders to identify and propose efficient strategic approach and policy implementations for integration of RES into the current power system in Vietnam. The results suggested that three major pillars should be considered to ensure the success of RES integration: strategic objectives, structural reforms and system transformation.

Equilibrium Pricing with Duality-Based Method: Approach for Market-Oriented Capacity Remuneration Mechanism

The crucial design elements of a good capacity remuneration mechanism are market orientation, insurance of long-term power system adequacy, and optimal cross-border generation capacity utilization. Having in mind these design elements, this research aims to propose a financially fair pricing mechanism that will guarantee enough new capacity and will not present state aid. The proposed capacity remuneration mechanism is an easy-to-implement linear program problem presented in its primal and dual form. The shadow prices in the primal problem and dual variables in the dual problem are used to calculate the prices of firm capacity which is capacity needed for long-term power system adequacy under capacity remuneration mechanism. In order to test if the mechanism ensures sufficient new capacity under fair prices, the mechanism is tested on the European Network of Transmission System Operators for Electricity (ENTSO-E) regional block consisting of Austria, Slovenia, Hungary, and Croatia with simulation conducted for a period of one year with a one-hour resolution and for different scenarios of the credible critical events from a standpoint of security of supply; different amounts of newly installed firm capacity; different short-run marginal costs of newly installed firm capacity; and different capacity factors of newly installed firm capacity. Test data such as electricity prices and electricity load are referred to the year 2018. The results show that the worst-case scenario for Croatia is an isolated system scenario with dry hydrology that results with high values of indicators expected energy not served (EENS), loss of load expectation (LOLE), and loss of load probability (LOLP) for Croatia. Therefore, new capacity of several hundred MW is needed to stabilize these indicators at lower values. Price for that capacity depends on the range of installed firm capacity and should be in range of 1000–7000 €/MW/year for value of lost load (VoLL) in Croatia of 1000 €/MWh and 3000–22,000 €/MW/year for VoLL of 3100 €/MWh that correlates with prices from already established capacity markets. The presented methodology can assist policymakers, regulators, and market operators when determining capacity remuneration mechanism rules and both capacity and price caps. On the other hand, it can help capacity market participants to prepare the most suitable and near-optimal bids on capacity markets.

A Novel Multi-Objective Based Reliability Assessment in Saudi Arabian Power System Arrangement

In a smart grid power system, reliability performance plays a crucial factor and requires additional focus. Moreover, the integration of Battery Energy Storage (BES) scheme, Solar Photovoltaic (SPV) and wind system in the smart grid system provide significant proficiency and reliability to the utilities. However, the grid coordination with the PV and other resources tends to cause major problems such as power interruption or else power outage. The outage of the power in the grid can cause power loss to the distribution system. Therefore, the novel reliability valuation of the smart grid system is developed for exaggeration of the SPV, wind and BES utilities based on the grid incorporation in Saudi Arabia. Furthermore, a novel Hobbled Shepherd Optimization (HSO) for boost converter control and Multi-Objective Based Golden Eagle (MOGE) algorithm for inverter control is proposed. The execution of this work has been done in MATLAB/Simulink. The simulation outcomes show that the projected method has attained the finest Total Harmonic Distortion (THD) and power loss. Also, the optimal reliability improvement has achieved by the projected methods while compared with the conventional methods in terms of Loss of Load Expected (LOLE), Loss of Load Probabilities (LOLP) and Expected Energy Not Supplied (EENS).

Reducing conventional generation dependency with solar Pv power: A case of Delhi (India)

ABSTRACT In several Indian cities, the governments is planning to reduce excessive air pollution due to fossil fuel used by traffic and local industries. This is done by encouraging greater use of renewable energy resources. One such city that has come under this observation is the Metropolis of Delhi, the capital of India. The Government of Delhi NCR has initiated corrective policy moves that promote the use of solar photovoltaic energy for minimizing dependency on fossil-fueled generation. However, it is well known that renewable energy has its limitations in relation to steady availability and desired quantity for bulk applications. Thus, at best, renewable energy can partially replace conventional fossil-fueled energy resources. The extent to which solar photovoltaic (SPV) energy can replace conventional-fueled generation is broadly referred to as capacity credit or capacity value of the concerned SPV. This paper is an attempt to compute the sizing of the optimum solar PV installation for a satisfactory level of capacity credit. The analysis employs the state sampling approach. The system adequacy is evaluated in terms of the reliability indices Loss of Energy Expectation (LOEE) and Loss of Load Expectation (LOLE). The Monte Carlo simulation technique is applied for the reliability evaluation, on account of its simplicity and better relative accuracy as compared with other available techniques. It is observed that for a solar penetration level of 30%, the increment in capacity contribution shows dissimilar trends with the increment in installation capacity. The optimum installation is witnessed for the 200 W and 400 W for the reliability criteria of LOLE and LOEE indices, respectively.

Evaluation of power plant reliability using index loss of load in the Suralaya power plant

Suralaya Power Plant (SPP) is the largest capable electricity generation company in Indonesia, with a total installed capacity of 3400 MW. As the largest power plant in Indonesia, SPP holds a great responsibility as a supplier 17% of the electricity needs of the Java, Madura and Bali (JAMALI) systems. This makes the SPP also one of the national vital objects (Obvitnas). This paper will calculate the reliability index of the generator using the method of loss of load probability (LOLP) and loss of load expectation (LOLE) based on the value of the force outage rate (FOR). Based on data on failure and daily expenses in 2019, the reliability index of SPP for units 1-4 is 4,23 days per year or 1,23%. The higher values LOLP/LOLE are in 2019 are possibility of SPP has lost its burden for 2 days in a period of 2019 because of force majeure (blackout) which causes a reduced load. To improve power plant is that the electricity load in West Java can be fulfilled with the electricity flow.

Reliability Analysis of Power Systems Integrated With High-Penetration of Power Converters

With the growing penetration of power electronic converters in power systems, the issue of reliability becomes more critical than ever before. This paper proposes a hierarchical reliability framework to evaluate the electric power system reliability from the power electronic converter level to the overall system level. On the converter level, the reliability model of a power electronic converter is developed based on the power electronic devices it is composed of, for which various hourly based input profiles and converter topologies are considered. On the system level, reliability metrics such as expected energy not served (EENS) and loss of load expectation (LOLE) are estimated through a non-sequential Monte Carlo simulation. Machine learning regression models, such as support vector regression (SVR), and random forests (RF) are implemented to bridge the nonlinear reliability relationship between two levels. The proposed framework is demonstrated through the modified IEEE Reliability Test System (RTS) 24-bus network. Numerical results show power converter reliability should be considered as an important factor when evaluating overall system reliability performance.

Incorporating climate change effects into the European power system adequacy assessment using a post-processing method

The demand-supply balance of electricity systems is fundamentally linked to climate conditions. In light of this, the present study aims to model the effect of climate change on the European electricity system, specifically on its long-term reliability. A resource adequate power system – a system where electricity supply covers demand – is sensitive to generation capacity, demand patterns, and the network structure and capacity. Climate change is foreseen to affect each of these components. In this analysis, we focused on two drivers of power system adequacy: the impact of temperature variations on electricity demand, and of water inflows changes on hydro generation. Using a post-processing approach, based on results found in the literature, the inputs of a large-scale electricity market model covering the European region were modified. The results show that climate change may decrease total LOLE (Loss of Load Expectation) hours in Europe by more than 50%, as demand will largely decrease because of a higher temperatures during winter. We found that the climate change impact on demand tends to decrease LOLE values, while the climate change effects on hydrological conditions tend to increase LOLE values. The study is built on a limited amount of open-source data and can flexibly incorporate various sets of assumptions. Outcomes also show the current difficulties to reliably model the effects of climate change on power system adequacy. Overall, our presented method displays the relevance of climate change effects in electricity network studies.

Managing Wind Power Generation via Indexed Semi-Markov Model and Copula

Because of the stochastic nature of wind turbines, the output power management of wind power generation (WPG) is a fundamental challenge for the integration of wind energy systems into either power systems or microgrids (i.e., isolated systems consisting of local wind energy systems only) in operation and planning studies. In general, a wind energy system can refer to both one wind farm consisting of a number of wind turbines and a given number of wind farms sited at the area in question. In power systems (microgrid) planning, a WPG should be quantified for the determination of the expected power flows and the analysis of the adequacy of power generation. Concerning this operation, the WPG should be incorporated into an optimal operation decision process, as well as unit commitment and economic dispatch studies. In both cases, the probabilistic investigation of WPG leads to a multivariate uncertainty analysis problem involving correlated random variables (the output power of either wind turbines that constitute wind farm or wind farms sited at the area in question) that follow different distributions. This paper advances a multivariate model of WPG for a wind farm that relies on indexed semi-Markov chains (ISMC) to represent the output power of each wind energy system in question and a copula function to reproduce the spatial dependencies of the energy systems’ output power. The ISMC model can reproduce long-term memory effects in the temporal dependence of turbine power and thus understand, as distinct cases, the plethora of Markovian models. Using copula theory, we incorporate non-linear spatial dependencies into the model that go beyond linear correlations. Some copula functions that are frequently used in applications are taken into consideration in the paper; i.e., Gumbel copula, Gaussian copula, and the t-Student copula with different degrees of freedom. As a case study, we analyze a real dataset of the output powers of six wind turbines that constitute a wind farm situated in Poland. This dataset is compared with the synthetic data generated by the model thorough the calculation of three adequacy indices commonly used at the first hierarchical level of power system reliability studies; i.e., loss of load probability (LOLP), loss of load hours (LOLH) and loss of load expectation (LOLE). The results will be compared with those obtained using other models that are well known in the econometric field; i.e., vector autoregressive models (VAR).

Risk-constrained stochastic optimal allocation of energy storage system in virtual power plants

Abstract This paper aims to develop a decision-making procedure for efficient placement and sizing of energy storage system (ESS) within virtual power plants (VPPs) premises under the uncertainty of market price. The main aim is to minimize the overall cost of VPP within a fixed time horizon planning. The understudy VPP consists of wind turbine, photovoltaic system, curtailable loads, ESS, and diesel generators in which the VPP trades electricity with the upstream grid. The proposed framework investigates both optimal power and optimal energy of ESS based on the available budget for investment. To hedge against the uncertainty of investment, an efficient risk management manner based on the concept of conditional value at risk is applied. Moreover, from the reliability point of view, two reliability indices: the loss of load expectation and energy expected not served, are evaluated under different levels of investment to assess the impact of ESS on VPP reliability. The proposed model is formulated as a mixed-integer nonlinear programming problem and is solved by the well-known General Algebraic Modeling System commercial software package. The effectiveness of the proposed risk-based optimization approach is demonstrated through vigorous case studies and comprehensive cost-benefit analysis.

Reliability Evaluation of Power Systems Containing Ocean Thermal Energy Conversion Power Plants

Ocean thermal energy conversion system uses from water in surface of the ocean as high temperature source and water in the depth of the ocean as low temperature source. Three types of ocean thermal energy conversion systems including close cycle, open cycle and hybrid systems are available. In a close cycle system, working fluid through a thermodynamic cycle based on the Rankine cycle can rotate the turbine and generate electricity. Due to the variation in the ocean surface temperature, the output power of the ocean thermal energy conversion system is not fixed and controllable and so this uncertainty nature results in the numerous states in the generated power of this plant. Thus, in integrating ocean thermal energy conversion systems to the power system, many aspects of power system such as reliability may be affected and so new approaches must be developed for investigation these effects. In this regard, in this paper for the first time, the reliability of power system containing ocean thermal energy conversion system is evaluated and the valuable indices such as loss of load expectation, expected energy not supplied and peak load carrying capability that can be used for generation expansion planning of power system, are calculated.

Probabilistic indices for analysing the impact of penetration of distributed energy resources on system reliability

Recent years have seen a steep increase in penetration of renewable energy sources (RES) based generators and storage devices; collectively referred as distributed energy resources (DERs). The traditional reliability indices used by utilities such as loss of load expectation, loss of energy expectation and loss of load probability are suitable for systems with conventional generators. However, they fail to acknowledge stochastic behaviour of RES based generators and energy limited capabilities of storage appropriately. In this study, new probabilistic reliability indices for investigating impact of penetration of DERs on system reliability have been proposed. In order to provide effective assessment, a reliability index referred as expectation of failure in grid connected mode (EFG) and three reliability indices viz. expectation of complete success in islanded mode, expectation of partial failure in islanded mode, expectation of complete failure in islanded mode have been proposed in grid connected mode and islanded mode, respectively. The mathematical formulation for proposed indices has been developed in probabilistic framework. The formulation has been implemented on a 33-bus distribution feeder. Different penetration levels have been investigated to provide adequate justification for proposed indices. Parametric analyses with storage integration have also been carried out. The proposed reliability metrics will assist system planners to choose optimum penetration level with greater efficacy.

Value of Loss Load Analysis of Java-Bali System Based on Macro Economic Data

Industrial and business sectors have big influence to gross domestic product in some regions (GDP). Beside that, GDP is influenced by electricity consumption. That statement is the reason why value of loss load (VOLL) had to be calculated. This research calculates VOLL for getting the value of outage cost, efficiency and productivity industrial and business sector in each Area Pengaturan Beban (APB) and in entire Java-Bali region. VOLL is calculated by macroeconomic analysis because this method has more time efficiency than survey analysis. VOLL forecasting in 2016-2023 are calculated too in this research. After that, the outage cost forecasting in hierarchical level 1 (HL 1) can be gotten if expected energy not supplied (EENS) from HL 1 is calculated. Before EENS calculation is held, analysis of another reliability indices such as loss of load expectation (LOLE) and loss of load probability (LOLP) were calculated. The calculation results show that VOLL in 2017 is 35,100.67 Rp/kWh and EENS in HL 1 is 7.25 MWh. Total outage cost from calculation is 289 million rupiahs.

Economical and Technical Assessment of Hybrid Renewable Energy Systems for Optimal Performance Applied in Hurghada, Egypt.

This paper deals with economic and technical evaluation of the hybrid power system which is fed six-megawatt load located in a city in Egypt called Hurghada. There is two types of economical evaluation. The first type is Economical evaluation which has two parts, the first part is cost estimation to know the price of generating power of the hybrid system in every season to reveal the lowest and the highest cost of the power unit in every season and The second part is an economic load dispatch for the six-megawatt load to know which hybrid system topology gives the required power at low cost and which topology gives the required power at a higher cost, economic load dispatch made for eight possible hybrid system topologies for four hybrid system units. The technical evaluation made by studying generating reliability indices Loss of Load Probability (LOLP), Loss of Load Expectation, (LOLE) Expected Energy Not Supplied (EENS) in every season of the year to know which season is more reliable to supply power to the load area. Studying generating reliability at the probabilities for generating units to achieve reliability index calculation according to the load duration curve of the load area in one year. The evaluation made by simulation of the hybrid system in MATLAB Simulink in every season and optimize power by using the Genetic Algorithm and Partial Swarm Optimization to ensure economical and technical optimal performance of the hybrid system.

Reliable Power Scheduling of an Emission-Free Ship: Multiobjective Deep Reinforcement Learning

Environmental pollutants, as a global concern, have led to a general increase in the utilization of renewable energy resources instead of fossil fuels. Accordingly, the penetration of these resources in all-electric ships, as well as power grids, has increased in recent years. In this article, in order to have a zero-emission and cost-effective energy management in an all-electric ferry boat, a new reliable and optimal power scheduling is presented that uses fuel cell and battery energy storage systems. Furthermore, the real information including load profile and paths is considered for the case study to assess the feasibility and superiority of the proposed approach. In addition to the cost of energy management, to have a reliable combination of the proposed resources, the loss of load expectation (LOLE) as a reliability index is considered in the energy management context and the problem is solved by the deep reinforcement learning in a multiobjective manner. The results of the consideration of two common standards, including DNVGL-ST-0033 and DNVGL-ST-0373, demonstrate that the proposed energy management method is applicable in industrial applications. Finally, the real-time simulation-based hardware-in-the-loop (HIL) is conducted to validate the performance and efficacy of the suggested power scheduling for the emission-free ships.

Can we phase-out all of them? Probabilistic assessments of security of electricity supply for the German case

Against the backdrop of expansions of intermittent renewable energy capacity, planned nuclear phase-outs, and current debates on the additional mothballing of coal-fired power plants in central Europe, there is a substantial increase in uncertainty regarding security of electricity supply. In this context, we present a probabilistic and scenario-based analysis of impacts on security of supply for Germany as a case-study in the medium-term perspective (years 2020, 2022, and 2023). For our analysis, we introduce the energy security assessment module of the JERICHO energy system model from RWTH Aachen University. Our model allows for an hourly probabilistic simulation of security of supply and accounts for stochastic characteristics of (non-)availability of conventional generators, renewable feed-in, electricity demand, and import potentials from central-European countries. To increase the robustness of results, we use a comprehensive dataset of temperature, wind speeds, solar radiation and water levels from weather years 1986 to 2015 as input for our simulations. Our results indicate that Germany is unlikely to maintain its quasi-absolute levels of security of supply in the near future with a mean Loss of Load Expectation (LoLE) of up to 2.6 h in scenario year 2023. However, this does not necessarily imply that future levels of security of supply are insufficient. There is need for further assessments on identifying threshold levels for security of supply, which are economically efficient and acceptable for society. Our results also indicate that the sensitivity of security of supply for meteorological changes increases with reductions of conventional power plants. Finally, we demonstrate that the dependency of security of supply on imports from neighboring countries increases substantially calling for increased international coordination of national energy policies.

Reliability evaluation of multi-carrier energy system with different level of demands under various weather situation

Multi-carrier systems enable the cooperation between different energy types to answer the electricity, water, heating and cooling demand together. One of the most important problems related to procuring energy for consumers is the reliability of delivered energy. This paper contributes a framework to assess the reliability of energy hub based on its elements and input energy carriers. Loss of load expectation (LOLE), loss of load probability (LOLP) and expected energy not supplied (EENS) have been employed to determine the reliability of the multi-energy carrier system in two different weather condition. The interactions between different energy carriers have been formed based on the exact model of energy storages. The calculation of the reliability assessment for energy hub has been performed by using MATLAB software. The results of the employment of the presented framework on a case study with various types of energy converters have been illustrated that the reliability of demands has been increased significantly by the use of an energy hub concept. Also, the reliability of energy hub has been imposed based on the size of components to recognize the impacts of each component on the general reliability of the system.

Tri-objective scheduling of residential smart electrical distribution grids with optimal joint of responsive loads with renewable energy sources

Abstract High penetration of renewable energy sources (RESs) and electrical energy storage (EES) systems in electrical distribution grids has changed the energy balance of distribution system operators (DSO). For this purpose, the energy scheduling problem of a Residential smart electrical distribution grid (RSEDG) with RESs and demand side management (DSM) is modeled as a tri-objective model consisting of: (1) minimization the operation cost, and emission pollutions in generation side; (2) minimization the loss of load expectation (LOLE) in demand side, and (3) minimization the deviation between the demand curve and output power of RESs. The third objective function is used as a DSM strategy to model the joint scheduling of RESs and the deferrable loads (DLs) where the DLs can be shifted regarding the output power of RESs to improve the load factor (LF). The uncertain behavior of the RESs are modeled using the stochastic optimization approach. The proposed model is solved using the epsilon-constraint method. Since the proposed approach has three objective functions; different Pareto solutions are obtained and the best solution is determined by the decision-making method. To investigate the effectiveness of the proposed method, it is applied on the 83-bus distribution grid and its results are compared for four cases studies.

Power System Market Planning

The loss of load expectation (LOLE) considered an acceptable security standard across most of the national electricity systems. In the present study, the LOLE is calculated. This means that the LOLE needs developing a model. The developed model serving to assesses the security of supply risks associated with the different electricity capacity margin levels, where the capacity margin is the level by which available electricity generation capacity exceeds the maximum expected level of demand. Then, the developed model updated on an annual basis. The annual update is helping to fulfill the Electricity Authority for planning, operation and control. The developed model will assess the system by calculating the capacity margin probability by combining both the generation and load models. The calculation of LOLE is an internationally accepted criterion in capacity adequacy. The results for the model of each region vary due to several factors, such as generation resource, load forecast, and forced outage rates (FOR). The estimation of the risk is economically optimal reserve margin to a number of case studies assumptions considered in the present study. The results help in future planning for the electric system operation and control. Furthermore, the study is helping to evaluate the implications of the obtained results for the electricity policy market to determine the best model for market design in the future.