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- Frequent escalation of fuel prices, concerns on environment and diminution of fossil fuel reserves have forced the incorporation of Renewable Energy resources with the existing generation. This paper presents an Economic Dispatch model developed for a system consisting of thermal units and Photo Voltaic (PV) plants. Since solar power is intermitt...
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Context 1
... FF is the fill factor, V MPPT ,I MPPT are the voltage and current maximum power point, V OC, I oc are the open circuit voltage and short circuit current of PV module, K v and K i are the voltage temperature coefficient and current temperature coefficient, T A , T CY ,N OT are the ambient temperature, PV cell temperature and Normal operating temperature respectively. The specifications of a 220 W PV module [17] are given in Table 2. The discrete solar distribution of the solar panel during 12 o clock of a day in winter is shown in Fig. 2 and the power variation during summer and spring is shown in Fig. 3. ...
Context 2
... dispatch is carried out for the updated demand and the objective function is optimised for three seasons and is analysed. The power generated and fuel cost of all the thermal units are given in Table 2. ...
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The purpose of this paper is to present a method for the probabilistic economic dispatch problem between the grid and a hybrid-microgrid. The probabilistic economic dispatch problem is solved for reducing the fossil fuel usage and the total cost of electricity consumption. A scenario tree method based on day-ahead load and wind power forecasting er...
Citations
... Several methods have been proposed for the optimal placement of wind turbines, employing approaches such as step-controlled primal-dual interior point methods [85][86][87][88], probabilistic load flow analysis [89,90], and Newton-Raphson methods for load flow studies [91]. Research has also focused on minimizing fuel and emission costs [92,93], utilizing techniques like the Ant Colony Optimization model [94][95][96], Tabu search optimization [97], and hybrid Fuzzy-GA methods [98]. These approaches have been applied to optimize DG placement for enhanced reliability, loss reduction, and voltage improvement [99][100][101][102][103]. ...
The literature on multi-attribute optimization for renewable energy source (RES) placement in deregulated power markets is extensive and diverse in methodology. This study focuses on the most relevant publications directly addressing the research problem at hand. Similarly, while the body of work on optimal location and sizing of renewable energy generators (REGs) in balanced distribution systems is substantial, only the most pertinent sources are cited, aligning closely with the study’s objective function. A comprehensive literature review reveals several key research areas: RES integration, RES-related optimization techniques, strategic placement of wind and solar generation, and RES promotion in deregulated power markets, particularly within transmission systems. Furthermore, the optimal location and sizing of REGs in both balanced and unbalanced distribution systems have been extensively studied. RESs demonstrate significant potential for standalone applications in remote areas lacking conventional transmission and distribution infrastructure. Also presents a thorough review of current modeling and optimization approaches for RES-based distribution system location and sizing. Additionally, it examines the optimal positioning, sizing, and performance of hybrid and standalone renewable energy systems. This paper provides a comprehensive review of current modeling and optimization approaches for the location and sizing of Renewable Energy Sources (RESs) in distribution systems, focusing on both balanced and unbalanced networks.
... With the increase in electric demand more burden is put on the generation of electricity from fossil fuel. To reducing the utilization of fossil fuels and greenhouse gases, convincing research has been conducted through the world for integrating and improvement the renewable energy sources into existing power systems [Sangroya, 2015] [Velamuri, 2015]. ...
Energy play a vital role in the lives of human being. With the increase in electric demand more burdens are put on the generation. More burden increases on the generating stations. This inflates the cost in electricity generation, to reduce this burden on generating stations and to provide relief to consumer, to cater all the issues certain method needs to be adopted to minimize the cost of generation. Wind and solar are cheap source of electricity but their reliability is inconsistent. Because these sources highly depend on the weather conditions. Thus, they cannot sup- ply power to load on their own. However, they can be used to form a hybrid power system. In this way, wind and solar will always supply the maximum power that they can and the remaining power demand is fulfilling by the conventional generators. In this paper we investigate the effect of integrating a wind and solar power into the Béni Ounif site electrical power system. in this study we will take a scenario for analyzing the impact of the integration of this renewable energy (wind / solar) on the dynamic economic dispatch problem solved with A Swarm Algorithm Intelligent Optimization PSO.
... FMINCON optimization solver methods utilize optional input, in addition to active sets and interior points chosen from the work in [40]. The authors adapted the work from [54][55][56] on the grid-tied solar PV and grid patterns hybrid energy systems' operational behavior and the co-optimization approach (EPD and EMS) using the following data: Vrms = 5000, 60 Hz, with an initial power of 10 MW, in a MATLAB environment using the FMINCON algorithm. Three-phase utility points of common connection data were used (Vrms = 6600, phase angle = 0.007, initial power 10 MW). ...
The requirement for the integration of power plants due to the cyclical rise in electrical energy consumption is due to the fluctuating load demand experienced with the current grid systems. This integration necessitates effectively allocating loads to the power plants for a minimum grid-tied transmission line cost, while meeting the network constraints. In this paper, we formulate an optimization problem of minimizing the total operational cost of all committed plants transmitted to the grid, while also meeting the network constraints and ensuring economic power dispatch (EPD) and energy management system co-optimization. The developed particle swarm optimization (PSO) method resolves the optimization problem using a piecewise quadratic function to describe the operational cost of the generation units, and the B coefficient approach is employed to estimate the transmission losses. Intelligent adjustments are made to the acceleration coefficients, and a brand-new algorithm is suggested for distributing the initial power values to the generation units. The developed economic power dispatch strategy successfully demonstrated an imperative cost reduction, with a connected load of 850 MW, 1263 MW, and 2630 MW of power demand, contrasted with previous PSO application cost values percentage, maximum yearly cost savings of (0.55%, 91.87), (46.55%, 3.78), and (73.86%, 89.10), respectively, and significant environmental benefits. The proposed co-optimization approach can significantly enhance the self-consumption ratio compared to the baseline method.
... FMINCON optimisation solver methods utilise optional input in addition to active set and interior point chosen from the work of [31]. The authors adapted the work of [46,47,48] on the grid-tied solar PV and grid patterns hybrid energy systems operational behaviour and co-optimisation approach (EPD & EMS), using the following data (Vrms =5000, 60 Hz, with an initial power of 10 MW) in a Matlab environment using the FMINCON algorithm. Three phase utility point of common connection data (Vrms = 6600, phase angle = 0.007, initial power 10 MW). ...
The requirement for integration of power plants due to the cyclical rise in electrical energy consumption is due to fluctuating load demand with the current grid systems. This integration necessitates effective allocating loads to the power plants for a minimum grid-tied transmission line cost while meeting network constraints. In this paper, we formulate an optimisation problem of minimising the total operational cost of all committed plants transmitted to the grid while meeting network constraints and ensuring economic power dispatch (EPD) and energy management system co-optimization. The developed Particle Swarm Optimization (PSO) method resolve the optimisation problem using piecewise quadratic function to describe the operational cost of the generation units, and the B coefficient approach is employed to estimate the transmission losses. Intelligent adjustments are made to the acceleration coefficients, and a brand-new algorithm is suggested for distributing the initial power values to the generation units. The developed economic power dispatch strategy successfully demonstrated an imperative cost reduction with connected load of 850MW, 1263MW and 2630MW power demand are contrasted with previous PSO application cost values, maximum yearly cost savings of (0.55%, 91.87), (46.55%, 3.78), (73.86%, 89.10) respectively, and significant environmental-benefit. The proposed co-optimisation approach can enhance a significant self-consumption ratio compared to the baseline method.
... Ethiopia is gifted with various renewable energy resources. The estimated potential for hydropower is 45 GW, geothermal is 5 GW, and solar irradiation ranges from 4.5 kWh/m 2 /day to 7.5 kWh/m 2 /day [13,14]. ...
In this study, a comparison of two artificial intelligence inspired solution methods employed to solve Security Constrained Economic Dispatch (SCED) of Ethiopian Renewable Energy Systems (ERES) is presented. The solution methods are Efficient & Parallel Genetic Algorithm (EPGA) and Hopfield Neural Network (HNN). This paper argues that employing intelligent SCED that considers power mismatch and intermittency of renewables can solve ERES’s recursive blackouts. A simulation was conducted on MATLAB. According to the results, both solution methods provide the best solutions for their respective purposes. For providing accurate forecast & predictive control of intermittent generation, it is imperative to employ HNN. When obtaining global maxima of multi-objective function is required, it is recommended to employ EPGA. Generally, employing intelligent SCED is a key planning step in adopting smarter grids as it reduces the production cost and the number of blackouts while increasing the security level of ERES.
... The uncertainty of solar irradiance can be modeled by using PDFs such as Weibull, beta, lognormal, logistics and gamma [26], where beta PDF is best suited for simulating randomness of irradiance data [16], [26]. Hence, beta PDF f t b (s t ) is used to model irradiance s t for each hour as follows [5], [16], [17], [27]: Fig. 1. Basic electricity market structure and area of concern of this paper. ...
... The shape parameters β t and α t , and gamma function Γ(×) of beta PDF are calculated using the mean value μ t and the standard deviation σ t of irradiance s t for the t th hour [5], [17], [27]. ...
... Similarly, certainty levels of 50% (p m = 0.5) and 60% (p m = 0.6) indicate the irradiance to be more than or equal to 0.60 kW/m 2 and 0.59 kW/m 2 , respectively, at the same time on the same day. s t pm is used to estimate the power output P t pv (s t pm ) of DSPVG for the t th hour as given in (5) [5], [17], [27]: ...
This paper proposes a simple and practical approach to model the uncertainty of solar irradiance and determines the optimized day-ahead (DA) schedule of electricity market. The problem formulation incorporates the power output of distributed solar photovoltaic generator (DSPVG) and forecasted load demands with a specified level of certainty. The proposed approach determines the certainty levels of the random variables (solar irradiance and forecasted load demand) from their probability density function curves. In this process of optimization, the energy storage system (ESS) has also been modeled based on the fact that the energy stored during low locational marginal price (LMP) periods and dispatched during high LMP periods would strengthen the economy of DA schedule. The objective of the formulated non-linear optimization problem is to maximize the social welfare of market participants, which incorporates the assured generation outputs of DSPVG, subject to real and reactive power balance and transmission capability constraints of the system and charging/dis-charging and energy storage constraints of ESS. The simulation has been performed on the Indian utility 62-bus system. The results are presented with a large number of cases to demonstrate the effectiveness of the proposed approach for the efficient, economic and reliable operation of DA electricity markets.
... CR and CP represent the reserve cost and penalty cost coefficients of wind power generation respectively. The reserve cost function helps to determine the debit that can be produced from the probability distribution function of variable wind speed [28] [29]. The probability of extracting desired power output from variable wind in the range of (vi ≤ v ≤ vr) can be determined by: ...
... Where K and C are Weibull probability distribution factors [29]. Moreover, solar PV's objective function considered as the third objective function is represented by f3(x): ...
This paper presents Security Constrained Economic Dispatch (SCED) of Renewable Energy Systems (RES) using Hopfield Neural Networks (HNN) to address power mismatch problems of the Ethiopian power grid. The mathematical formulations of SCED for RES comprising biomass, hydro, solar PV, waste to energy plant, wind, and geothermal are presented. Each of these sources requires problem formulation and constraint handling mechanisms considering security limits and credible contingencies. This enables renewable energy systems to provide secure and reliable electric service. Modified IEEE 118 bus system and Ethiopian renewable energy systems were used as case studies. Modelling and simulation were conducted on MATLAB. According to the results obtained, it can be deduced that employing HNN based SCED is a promising step in connection to developments needed in the adoption and realization of smarter grids as it reduces execution time, production cost and the number of blackouts while increasing the security level of a power system of RES.
... Probability distributions have also been employed in the power industry, for example, to predict the output of a wind or solar farm. Wind power characteristics are commonly modelled using the Weibull distribution [6][7][8][9], and the beta distribution is used for solar power [10,11]. ...
Coal power plants produce about 38% of the electricity around the world and are a major source of CO2. The CO2 emitted by these plants has to be captured and transported to a storage site as part of the CO2 mitigation strategy defined by Carbon Capture and Storage (CCS). The most economical method for long-term onshore CO2 transport is by pipeline, and it is recognised that the CO2 flow rate assumed for pipeline design has an impact on transport costs. Therefore, it is important to investigate the impact of the variability of flow rate in a CO2 pipeline network on its design and economics. CO2 flow rate is a strong function of power plant load. The latter is affected by seasonal and diurnal changes, rarer events such as a global recession, variations in coal prices and climate change, which can impact the demand. Besides plant load, the instantaneous CO2 flow rate is also a function of capture rate and emission intensity. The combination of all these factors makes power plant load behaviour complex and random, making it a candidate for stochastic treatment. Several studies have been conducted for CO2 pipeline optimisation but none of these investigate the randomness in behaviour of CO2 flow rate. Therefore, this analysis presents a stochastic analysis of the flow rates used for the optimisation of the design of a proposed CO2 pipeline network.
The CO2 sources used in this analysis are the black coal-fired power plants located in NSW, Australia. These plants use bituminous coal as fuel, which has a high HHV/LHV, i.e. the amount of heat released by unit mass or volume of fuel (initially at 25 °C) once it is combusted and the products have returned to a temperature of 25 °C. Therefore, these power plants are good candidates for CCS, having high mitigation potential. The analysis in this work is applicable to the expected range of CO2 flow rates around the world, as the CO2 sources have a rated capacity ranging from 1,320 MW to 3,000 MW, equivalent to CO2 flow rates of 1 to 20 Mtpa. The load data is investigated for a period of two consecutive years, spaced over 5-minute intervals, to observe the trends in the flow behaviour such as seasonality and diurnal patterns.
For real cases, the CO2 flow is expected to change with time as the power plant load varies, which necessitates a dynamic flow analysis. However, if the variation in load is gradual, the dynamic state can also be treated as quasi-steady. Therefore, this analysis is conducted assuming quasi-steady state conditions. The approach used involves fitting multi-modal probability models to the distribution of flow rate of CO2 captured from the power plant. Three distributions types are evaluated; Normal, Logistic-normal and Gamma, which are used to model the cumulative probability distribution of the flow in terms of time. The load data are compared using goodness of fit measures to observe their conformance with analytical probability distribution behaviour. The probability distribution with the best goodness of fit to the actual plant data is then chosen for predicting the percentage of time the flow rate takes a specific range of values, and this information is used in the design of the CO2 pipeline network.
The probability distribution of flow rates is then used to determine the probability distribution of the operating costs of the pipeline network. Depending on the flow rate chosen for pipeline design, the pipeline may be over or under designed over a given time period and this impacts the annual operating costs. For example, if the average flow rate is chosen as the design flow rate, then the pipeline will be under designed for the times when flow rate is higher, and vice versa. During the time when operating flow rate exceeds the design flow rate, the operating costs will be higher than estimated. However, there is a trade-off between the operating costs and the time for which the pipeline is under-utilized (over-designed) or under-designed. This analysis helps to identify the conditions under which is it more suitable to under or over design the pipeline, given the probability distribution.
... Solar PV: the solar power output that can be extracted from a given solar irradiance G is [14,22]: ...
This paper presents Security Constrained Economic Dispatch (SCED) of renewable energy systems (RES). Reformulation of SCED for RES comprising biomass, large and micro-hydro plants, solar PV, solar thermal, waste to energy plant, wind farm and geothermal has been carried out. This enables RES prime-moved power systems provide secure and reliable service. Each of these sources requires problem formulation and constraint handling mechanism that take into account security limits and credible contingencies. Modified IEEE 118 bus system (NREL-118 test system), Ethiopian renewable energy system, and modified New England 39-bus system with high RES penetration features were used as case studies. Modeling and simulation was conducted on MATLAB, MATLAB/MATPOER, and DIGSILENT power factory simulation platforms. According to the simulation results obtained, it is deduced that the economic dispatch of RES is a promising step in connection to developments needed in the adoption and realization of smarter grids.
... A substantial number of SCED with respect to renewable integration studies have focused on optimization requirements of power systems with high renewable penetration such as wind [3] natural gas [7] photovoltaic (PV) [28]- [29]. ...
This paper presents a selective survey of papers, books, and reports that articulate recent trends of Security Constrained Economic Dispatch (SCED) of integrated renewable energy systems (IRES). The time-period under consideration is 2008 through 2020. This is done to provide an up-to-date review of the recent, major advancements in the SCED, and state-of-the-art since 2008. This helps identify further challenges needed in adopting smarter grids, and indicate ways to address these challenges. The study was conducted in three areas of interest that are relevant for articulating the recent trends of SCED. These areas are (i) SCED of power systems with IRES, (ii) SCED mathematical formulation and solution methods, and (iii) SCED challenges. The review results and research directions deduce that the state of the art research is not enough and needs special attention on following the path of artificial intelligence-based optimization.
