A Fuzzy Unit Commitment Model for Enhancing Stability and Sustainability in Renewable Energy-Integrated Power Systems

Guest Editorial: Situational awareness of integrated energy systems

In recent decades, voltage instability has become an important contributor to large-scale power outages globally. Periodic voltage security assessments are key for the secure operation of power systems. There are various indicators which have been implemented in different types and complexity of systems to provide a measure of closeness to voltage collapse, but some of these algorithms may require advanced computational resources or may not perform well, depending on the prevailing conditions. Expanding interconnected power systems is needed like never before due to the increasing world demand for electricity, and voltage stability is therefore a worth consideration. The growing demand penetration of solar photovoltaic (PV) systems has some potential stability challenges due to their intermittent characteristics. Existing research evaluates the test systems of voltage stability and does not optimize using Intelligent models, which have variable power generation from Solar PV and Wind, as well as uncertainties in load demand and the initial step considering the evaluation of the model have to cover either inputs. A feed–forward back propagation artificial neural network (ANN) for voltage stability assessment to classify the operating scenario of a power system into one of two states, i.e. safe or unsafe. Neural network input data is generated from load flow analysis using Newton-Raphson (NR), and stability indices are computed over different scenarios. Line Stability Index (Lmn), counts the stability of a transmission line in power systems and provides an indication of its potential voltage collapse. The Fast Voltage Stability Index (FVSI), which also estimates line stability through reactive power and impedance characteristics. Voltage Collapse Proximity Index (VCPI) assesses system proximity to voltage collapse and identifies weak transmission lines. Values approaching 1 suggest voltage collapse conditions. The FVSI, VCPI, and Lmn are compared to a baseline NR stability index. Moreover, self-organizing map (SOM) neural network is used for stability results classification. Multi-objective grasshopper optimization algorithm (MOGOA) has been employed as an optimization network on the dual axis solar PV and wind turbine-based hybrid power generation system to analyze technical, economic, as well as environmental relations of energy conversion. The findings reveal that the adapted grid-tied solar-wind composite power system substantially mitigates the power deficiencies, reduces the real-power outages, diminishes the motion voltage small amendments, maximizes voltage prosperity, and improves power system resilience. The main findings show a significant reduction in real power loss, which went from 289,391.30 kW to only 5.43 kW, and a remarkable decrease in voltage deviation, which reduced from 0.56 p.u to 0.0493 p.u. Ensures Improved Service Quality and Enhanced Equipment Protection While gas generation costs increased by a small amount, it is important to note that this is an acceptable trade-off for a more robust and sustainable system, with costs per kWh of $0.142 vs $0.2881 per kWh, respectively. In addition, stability index was substantially decreased from 0.7666 to 0.0768, indicating better grid and resilience to disturbances. Findings emphasize the contribution of embedding MOGOA in conjunction with machine learning models including ANN and Self Organizing Maps (SOM) to fulfill the primal requisites of stability, efficiency and sustainability in modernized power systems, illustrating a coupled advancement in the realm of power systems engineering and optimization.

The high penetration of inverter-fed renewable energy sources (RESs) in modern energy systems has led to a reduction in the system’s inertial response. This reduction in the rotational inertial response is associated with synchronous generation and might result in a deteriorated frequency response following a power disturbance. This paper investigates the frequency stability of the Kingdom of Saudi Arabia’s (KSA) grid. It includes a description of the changing energy landscape of the KSA’s electricity grid and an investigation of the impact of high penetration levels of inverter-fed RESs on the dynamic behavior of the KSA grid. The impact of RESs has been studied through a simulation of case studies of the future KSA power system using the MATLAB/Simulink simulation software. The frequency stability of the KSA’s power system has been evaluated with various RES levels under peak and base load conditions. The simulation results show that the high penetration levels of RESs dramatically affect the system’s frequency response, especially under off-peak conditions. In addition, the significance of battery energy storage systems (BESSs) for compensating the reduction in the system inertial response has been addressed. The results show the effectiveness of aggregated BESSs for enhancing the system frequency control of the KSA grid.

Evaluating the inertia of the Jordanian power grid

In this paper the operation of small isolated island grids is investigated with the presence of centrally managed battery energy storage (BES) facilities. A mixed integer linear programming (MILP) unit commitment and economic dispatch (UC-ED) method is utilized to evaluate the impact from the introduction of BES on system operation. Two small non-interconnected island (NII) power systems with diversified characteristics are examined under high renewable energy source (RES) penetration conditions and the anticipated benefits for the system from the presence of storage are quantified in terms of security, economics and achievable renewable energy penetration levels in each case.

The importance of probabilistic assessment in distribution systems is very high due to the increasing penetration of renewable energy sources (RESs) with their fluctuating behavior. Also, there are some other uncertain variables in distribution systems such as loads fluctuations. More awareness of the system state by considering many uncertainties provides more certainty in decision making and causes better risk management. Moreover, the distribution static compensator (DSTATCOM) has been recently implemented due to its efficient abilities in the distribution systems operation especially when RESs are integrated into them. This article investigates the probabilistic assessment of DSTATCOM operation in a distribution system using the k-means-based data clustering method (DCM). The uncertainty of load demands, wind speed, and solar radiation are considered in this study. The performance of DCM is compared to the Monte Carlo simulation (MCS) and Latin Hypercube sampling (LHS) methods in terms of accuracy and computational burden. The efficiency of k-means-based DCM is investigated in the IEEE 69-node test system.

Chapter 9 - Integrated Planning of Energy and Water Supply in Islands

Optimal scheduling of distributed battery storage for enhancing the security and the economics of electric power systems with emission constraints

With the recent growing interest in renewable energy integrated power systems across the globe for the various economic and environmental benefits, it is also significant to consider their influence on voltage stability in power systems. Therefore, this paper reports the static voltage stability impact of solar photovoltaic generation on power networks using PowerWorld simulator power-voltage (P–V)- and voltage-reactive power (V–Q)-curves to investigate the renewable energy generator model performance suitability. The impact of varying power factor control and static voltage droop control of a photovoltaic plant on the maximum generated power, threshold voltage profile and reactive power marginal loading has been examined. Besides, the concept of percentage change in voltage-power sensitivity has been systematically utilized to determine the optimal location for the solar photovoltaic generator on the power grid and the feasible penetrations have been defined for selected system buses. From the simulation results it can be concluded that in a steady-state analysis of the grid integrated power system the effects of power factor (pf) control and voltage droop control should be considered by power grid engineers for effective system operation and, equally, the application of percentage change in voltage-power sensitivity should be extended to real networks to determine the best positions for multiple installations of renewable energy resources.

The enhancement of economic sustainability and the reduction of greenhouse gas (GHG) emissions are becoming more relevant in power system planning. Thus, renewable energy sources (RESs) have been widely used as clean energy for their lower generation costs and environmentally friendly characteristics. However, the strong random uncertainties from both the demand and generation sides make planning an economic, reliable, and ecological power system more complicated. Thus, this paper considers a variety of resources and technologies and presents a coordinated planning model including energy storage systems (ESSs) and grid network expansion, considering the trustworthiness of demand-side response (DR). First, the size of a single ESS was considered as its size has a close effect on maintenance costs and ultimately affects the total operating cost of the system. Second, it evaluates the influence of the trustworthiness of DR. Third, multiple resources and technologies were included in this high-penetration renewable energy integrated power system, such as ESSs, networks, DR technology, and GHG reduction technology. Finally, this model optimizes the decision variables such as the single size and location of ESSs and the operation parameters such as thermal generation costs, loss load costs, renewable energy curtailment costs, and GHG emission costs. Since the problem scale is very large not only due to the presence of various devices but also both binary and continuous variables considered simultaneously, we reformulate this model by decomposition. Then, we transform it into a master problem (MP) and a dual sub-problem (SP). Finally, the proposed method is applied to a modified IEEE 24-bus test system. The results show computational effectiveness and provide a helpful method in planning low-carbon electricity power systems.

Unit Commitment (UC) is an essential decision taken by ISO to ensure reliable and economic operation of the power system. Renewable energy penetration is increasing in a power system due its inexhaustive and environment friendly nature. Renewable energy poses some challenges to the present power systems due to its high uncertainty. In addition to this, the impact of contingencies on the power system also cannot be ignored. Both renewable energy and contingencies make the power system more prone to uncertain behaviour which can affect the economics as well as reliability of the power system. In this paper, UC is solved using deterministic and stochastic approaches. Deterministic UC considers forecasted wind data and Stochastic UC considers uncertain scenarios of the wind. Practically the power system is more prone to uncertainty because of renewable energy penetration and contingencies. Thus, this paper discusses how a UC decision varies as more uncertainties arises. Mixed Integer Linear Programming (MILP) is used for obtaining the UC decision results for IEEE 30-bus system. The results of UC, power dispatch and generation costs obtained with the two approaches have been compared.

Economical operation of modern power grids incorporating uncertainties of renewable energy sources and load demand using the adaptive fitness-distance balance-based stochastic fractal search algorithm

Reliability assessment and congestion management of power system with energy storage system and uncertain renewable resources

Communication routing of cyber‐physical power systems (CPPS) with the high penetration of renewable energy sources (RES) plays an important role in the resilience enhancement against disasters, natural and man‐made alike. Therefore, a trilevel optimization model is proposed for the CPPS resilience enhancement with the RES against extreme events. The upper‐level model identifies optimal hardening lines of both the transmission and communication networks with consideration of the communication routing constraints. The middle‐level model identifies the attacked lines to maximize load shedding of the power system. The effects of RES uncertainty and communication routings on resilience enhancement are analysed. The lower‐level model attains an optimal allocation strategy of power generation to minimize load shedding. The model is solved by the column‐and‐constraint generation algorithm. Case studies are conducted on the IEEE 14‐bus, RTS 24‐bus, and 118‐bus system. The results show that the proposed hardening strategy can effectively ensure the adaptiveness of the transmission network and communication routing to improve the resilience of CPPS to extreme events. Moreover, it is observed that the load loss and total investment cost are heavily affected without and with RES.

The penetration of renewable energy sources (RESs) in the electrical power system has increased significantly over the past years due to increasing global concern about climate change. However, integrating RESs into the power market is highly problematic. The output of RESs such as wind turbines (WTs) and photovoltaics (PVs) is highly uncertain. Their correlation with load demand is not always guaranteed, which compromises system reliability. Distributed energy resources (DERs), especially demand response (DR) programs and energy storage systems (ESSs), are possible options to overcome these operational challenges under the virtual power plant (VPP) setting. This study investigates the impact of using a DR program and battery energy storage system (BESS) on the VPP’s internal electricity market, and also cost-minimization analysis from a utility viewpoint. Three different constrained optimal power flow (OPF) problems are solved such as base case, DR case, and BESS case to determine total incurred costs, locational marginal prices (LMPs), and generator commitments. A scenario tree approach is used to model the uncertainties associated with WTs, PVs, and load demand. The proposed model is investigated on a 14-bus distribution system. The simulation results obtained demonstrate a favorable impact of DR and a BESS on renewable operational challenges.