Robust approach for capacity benefit margin computation with wind energy consideration for large multi-area power systems

Capacity benefit margin (CBM) represents the tie-lines transfer capability margin for power interchange between interconnected areas. Accurate evaluation of CBM is essential for available transfer capability (ATC) determination. Most of the existing methods for CBM computation rely on complex optimization techniques. In these techniques, for every step increase in power transfer, to improve supply reliability of the deficient areas, the reliability must be recalculated and checked through optimization. Thus, for a large number of interconnected areas, these techniques might not scale well. Another shortcoming of these techniques is the simplifying assumption of only one deficient area with a fully connected network (i.e., all the areas have a direct connection or tie line with each other). In this thesis, a robust graph-theoretic approach is proposed to calculate CBM in a multi-area network with multiple deficient non-directly connected areas. Unlike the existing approaches, multiple deficient areas are considered and some of the areas are not fully connected. From literature, previous techniques only considered conventional generating units in the loss of load expectation (LOLE) computation. A strategy for the incorporation of wind power generating unit is proposed using Weibull probability distribution. This is important since the supply reliability of an area is measured using LOLE of the area and considering the random nature of wind generating systems which has a great effect on the supply reliability. In addition, LOLE which is commonly used as an index for the CBM computation is usually evaluated by using the area peak load demand and the available reserve capacity. The system peak demand usually occurs within a few weeks in a year; therefore, the period of off-peak demand is not efficiently accounted for in the LOLE evaluation. Hence, demand side management (DSM) resources; peak clipping and valley filling are employed to modify the chronological load model of the system which subsequently enhances the CBM quantification. Finally, the results of the CBM are incorporated in ATC computation to study the influence on the ATC evaluation. The proposed technique has been evaluated using IEEE RTS-96 test system because the system has all the required reliability data for LOLE computation. The technique can evaluate and allocate CBM among multi-area systems consisting of two deficient areas. The influence of renewable energy on LOLE has been efficiently evaluated and the DSM technique was efficiently employed to improve three-area test system generation reliability. The generation reliability of the interconnected areas has been improved by an average of 35%. This improvement is very significant in terms of the generation facilities and the financial implication that may be required to be put in place if the proposed DSM technique was not applied. The results and the performance evaluation showed that the proposed technique is simple and robust compared to the existing methods. The technique can also be used as a feasibility tool by utilities to verify the possibility of wheeling power to a deficient area using maximum flow algorithm

Solar radiation forecasting in nigeria based on hybrid PSO-ANFIS and WT-ANFIS approach

For an effective and reliable solar energy production, there is need for precise solar radiation knowledge. In this study, two hybrid approaches are investigated for horizontal solar radiation prediction in Nigeria. These approaches combine an Adaptive Neuro-fuzzy Inference System (ANFIS) with Particle Swarm Optimization (PSO) and Wavelet Transform (WT) algorithms. Meteorological data comprising of monthly mean sunshine hours (SH), relative humidity (RH), minimum temperature (Tmin) and maximum temperature (Tmax) ranging from 2002-2012 were utilized for the forecasting. Based on the statistical evaluators used for performance evaluation which are the root mean square error and the coefficient of determination (RMSE and R²), the two models were found to be very worthy models for solar radiation forecasting. The statistical indicators show that the hybrid WT-ANFIS model’s accuracy outperforms the PSO-ANFIS model by 65% RMSE and 9% R². The results show that hybridizing the ANFIS by PSO and WT algorithms is efficient for solar radiation forecasting even though the hybrid WT-ANFIS gives more accurate results

A hybrid PSO-ANFIS approach for horizontal solar radiation prediction in Nigeria

For efficient and reliable hydrogen production via solar photovoltaic system, it is important to obtain accurate solar radiation data. Though there are equipment specifically designed for solar radiation prediction but are very expensive and have high maintenance cost that most countries like Nigeria are unable to purchase. In this study, the accuracy of a hybrid PSO-ANFIS method is examined to predict horizontal solar radiation in Nigeria. The prediction is done based on the available meteorological data obtained from NIMET Nigeria. The meteorological data used for this study are monthly mean minimum temperature, maximum temperature, relative humidity and sunshine hours, which serves as inputs to the developed model. The model accuracy is evaluated using two statistical indicators Root Mean Square Error (RMSE) and Coefficient of determination (R²). The accuracy of the proposed model is validated using ANFIS, GA-ANFIS models and other literatures. Based on the statistical parameters used for the model evaluation, the results obtained proves PSO-ANFIS as a good model for predicting solar radiation with the values of RMSE=0.68318, R²=0.9065 at the training stage and RMSE=1.3838, R²=0.8058 at the testing stage. This proves the potentiality of PSO-ANFIS technique for accurate solar radiation prediction

Threats and challenges of smart grids deployments - a developing nations’ perspective

Considerable efforts in huge investments are being made to achieve a resilient Smart Grids (SGs) deployment for the improvement of power delivery scheme. Unsurprisingly, many developing nations are making slow progress to the achievement of this feat, which is set to revolutionize the power industry, own to several deployment and security issues. Studying these threats and challenges from both technical and non-technical view, this paper presents a highlight and assessment of each of the identified challenges. These challenges are assessed by exposing the security and deployment related threats while suggesting ways of tackling these challenges with prominence to developing nations. Although, a brief highlight, this review will help key actors in the region to identify the related challenges and it’s a guide to sustainable deployments of SGs in developing nations

A graph-theoretic approach to capacity benefit margin calculation for large multiarea power systems

In a deregulated power market, independent system operators exchange information related to the available transfer capability (ATC) of their respective areas for efficient power system operation. Accurate ATC calculation is important for efficient market operations. Among the parameters required to calculate ATC, the capacity benefit margin (CBM) is an important one. CBM represents the interarea tie-lines capacity reserved in order to have access to external generation in an event of power supply shortage. This paper proposes a new technique for CBM evaluation using graph theory concepts. The proposed technique can be used as an effective tool to solve the CBM problem for large multiarea power systems which are not fully connected and may have multiple deficient areas, unlike previous approaches which considered only fully connected areas with only one deficient area. The IEEE 24-bus reliability test system is employed to implement the proposed method. A performance analysis of the proposed technique shows that it can find a feasible solution efficiently

Techno-economic feasibility analysis of an off-grid hybrid energy system for rural electrification in Nigeria

Rural electrification improves the quality of life and wellbeing of the rural communities. Diesel generators and woods are the main source of energy for the rural dwellers who are not connected to electricity grid, these sources of energy are not cost effective and are detrimental to the community health due to the release of gaseous pollutants from these sources of energy. Therefore, the use of renewable energy sources as an alternative source of energy for the rural communities become imperative in order to improve socioeconomic activities of these rural communities. In this study, a feasibility analysis on the use of a hybrid solar-wind-battery-diesel system for providing electricity to a rural secondary school is investigated. A village (Moriki) in north western Nigeria is selected for this study with the aim of taking the same study to other parts of the country. A simulation software Hybrid Optimization Model for Electric Renewable (HOMER) is employed to carry out the feasibility study to come up with an optimal configuration in terms of Net Present Cost (NPC) and Cost of Energy (COE). Hybrid solar PV-battery system is the optimal configuration simulated by HOMER with NPC of $18,161 and COE of$0.233/kWh was obtained for a sensitivity case of 6% nominal discount rate. Sensitivity analysis was carried out where wind speed, solar radiation, and nominal discount rate were considered as the sensitive parameters to investigate their impacts on the NPC and COE, the result shows that the sensitivity variables has impact on the NPC and COE. The results also showed a magnificent reduction in greenhouse gas (GHG) emission by 100% because the optimal configuration has 100% renewable fraction

Fraud detection for metered costumers in power distribution companies using C5.0 decision tree algorithm

Non-technical Losses mainly Electricity theft has been a main concern for power utilities from last many years. Power utilities are estimated to lose billion dollars annually because of illegal usage of electricity by fraudulent consumers. Researchers are trying different methods for proficiently recognizing fraudster costumers. This research suggests a new approach based on C5 algorithm for efficiently identifying consumers involved in electricity theft. The C5.0 algorithm is a modified form of the C4.5 algorithm. It is also one of the decision tree algorithms but with a much-improved classification rate. The C5.0 algorithm relies on monthly energy consumption data to identify any anomaly in consumer energy usage data associated with NTL behavior. There are many types of fraud committed by fraudulent consumers but this research is focused on fraudulent consumers who have a unexpected deviation from their usual load profile. The motivation of this research is to aid Power distribution companies in Pakistan to decrease there NTL’s due to pilfering in energy consumption by fraudulent consumers. The accuracy of the C5.0 algorithm is 94.61% which is much higher when compared to some state of the art machine learning algorithms like Random forest, Support Vector Machine, K-NN and other decision trees

Capacity benefit margin assessment in the presence of renewable energy

Energy sustainability and reliability issues have created great concerns globally, instigating various utilities to create interconnections with one another for system security and techno-economic benefits. Capacity benefit margin (CBM) is the amount of transmission capacity reserved between interconnected systems for emergency power exchange between utilities. It is usually estimated by evaluating the reliability of the generating units of interconnected systems to know the amount of external generation required during emergency supply shortage. Various techniques have been employed in literature to evaluate CBM. However, despite the global awareness on the continuous shift from conventional power generation sources to renewable energy sources, the assessment of CBM in the presence of renewable energy has not been addressed. Moreover, existing techniques for CBM calculation are based on iterative optimization and may not scale well to more than one deficient area. To solve these issues, this article incorporates wind power (WP) generation in CBM computation. The proposed technique is based on graph theory and can be used to calculate CBM in the presence of multi-deficient areas and renewable energy sources, in particular, WP. Simulations using the IEEE 24-bus reliability test system in MATLAB show that the proposed technique is significantly better in terms of scalability and accuracy as compared to existing techniques. The proposed technique can be employed by utilities for CBM estimation in the presence of renewable energy sources

Penalization of electricity thefts in smart utility networks by a cost estimation-based forced corrective measure

Electricity theft menace has attracted various research efforts with most proposed detection algorithms relying on analysing customers' consumption profile to determine fraudulent electricity consumers (FEC). This necessitates the need for on-site inspections before penalties are sanctioned despite the manpower, cost, energy, time, and stress associated with such tedious routine. Moreover, the penalty-imposed fines are bogusly determined and uncoordinated, and losses in revenue are burdened on the honest consumers. Fortunately, the advent of advanced metering infrastructure offers a flexible and efficient platform which can be leveraged to provide additional functionality of curbing these complicated procedures. In this work, a cost estimation-based model deploying a forced corrective measure for a real-time enforcement of penalties on FEC in a smart utility network is proposed. It relies on the results of commonly applied intelligent algorithms for electricity theft detection to obtain the amount and cost of energy consumed by reported FEC while also providing efficient monitoring till imposed fines are cleared. The results of the developed model give proportionate sanctions and enhances the functions of the system manager's monitoring of the operational status to ensure compliance and is suitable for deployment in a smart utility network

Model based predictive control strategy for water saving drip irrigation

Traditional irrigation control systems is characterized with inefficient management of water and often results in low water productivity index and reduced cultivation yield. In addition, insufficient water supply and high rate of water loss due to evapotranspiration increases plant stress which often affects its growth and development. Therefore, to address this issues, this paper is aimed at developing a model predictive control (MPC) strategy for water saving drip irrigation experiment that will regulate the soil moisture content within the desired field capacity and above the wilting point, while scheduling irrigation to replace the loss of water from soil and plant due to evapotranspiration in the greenhouse environment. The controller design involves a data driven predictive model identified and integrated with the MPC designer in MATLAB and thereafter exported in Simulink for simulation. The generate controller code was modified and deployed on a Raspberry Pi 4 controller to generate a pulse width modulated signal to drive the pump for the control water mixed with fertilizer. To achieve enhancement of controller an Internet of Things (IoT) integration was used for easy soil, weather, and plant monitoring which are used to update the MPC model for the irrigation control. The performance of the proposed MPC controller deployed drip irrigated Greenhouse(GH1) is benchmarked against an existing automatic evapotranspiration (ETo) model based controller in Greenhouse(GH2), with each greenhouse containing 80 poly bags of Cantaloupe plant with similar growth stage. The results obtained shows that, the proposed MPC-based irrigation system has higher water productivity index of 36.8 g/liters, good quality of fruit with average sweetness level of 13.5 Brix compared to automatic ETo-based irrigation system with 25.6 g/liters and 10.5 Brix, respectively. However, the total mass of harvested fruit for ETo-based irrigation system is higher than MPC-based irrigation system by 21.7%. The performance of the proposed MPC controller was achieved through the integration of event based scheduling with IoT monitoring as well as inclusion of evapotranspiration effect in the plant dynamics