Spinning Reserve Capacity Research Articles

Short-Term Electrical Energy Demand Forecasting Using Fuzzy Logic Technique

Minimization of electrical energy wastage and provision of its regular supply make electrical load forecasting an important aspect of the power infrastructure. An accurate load forecasting is crucial for the reduction of the cost of electrical energy generation and spinning reserve capacity. Therefore, in this study, fuzzy logic (FL) technique was employed for the projection of electrical energy demand on short-term basis. The FL model was trained using the six months hourly load and temperature data respectively obtained from 132/33 kV Ikeja West Transmission Station, Ayobo and Nigerian Meteorological Agency, Oshodi, Lagos State, Nigeria. Triangular and trapezoidal membership functions (MFs) were used in the training of the model with Mamdani fuzzy inference system. The time, load and temperature inputs were fuzzified into six, four and three MFs respectively while the fuzzification process used 25 fuzzy rule bases. The centroid method of defuzzification was used to change the results into readable values. The adequacy of the FL model was determined using five metrics including mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), chi square (χ2) and F- test. The obtained results revealed that the FL model developed excelled in all the five tests of adequacy considered with MSE, MAE, MAPE, χ2 and F-test values of 4.17, 6.74, 11.51%, 7.93 and 1.27 respectively and hence, performed satisfactorily. This work established that the fuzzy logic approach is appropriate for electrical load projection on short-term basis.

An Effective Spinning Reserve Allocation Method Considering Operational Reliability With Multi-Uncertainties

Spinning reserve (SR) is a crucial resource for ensuring power system reliability by lowering operational risk. The operational risk is contingent on the unpredictability of renewable energy as well as the replacement rate of electrical devices in relation to operating conditions. Previous research on SR allocation ignoring the condition-dependent outage replacement rate (CDORR) of electrical equipment may have allocated insufficient SR capacity that cannot adjust for unanticipated power imbalances. In addition, the introduction of CDORR and various multi-uncertainty scenarios into existing SR allocation models may incur a significant computational cost. This work presents an efficient SR allocation model that takes operational reliability under multi-uncertainties into account (e.g., wind output randomness, load fluctuations, generator and transmission contingencies with CDORR). Analytical expected energy not served (EENS) formulations based on the sensitivity method are derived to estimate the operational risk under multi-uncertainties, thereby obviating the need for iterations in large uncertainty scenarios. We present an improved relaxation method based on the McCormick envelope to further enhance the model's tractability. The proposed tangent plane cut collection strategy improves the computational efficiency by reducing the redundant envelope region. Results demonstrate that the proposed method benefits the economy by considering operational reliability and accelerates computational speed with reasonable accuracy.

Short term load forecasting using evolutionary algorithm for Tajikistan

<p>Load forecasting is a significant element in the energy management system of power systems. Precise load forecasting aids electric utilities to conduct decisions of unit commitment, reduction of spinning reserve capacity, and schedule device maintenance plan. Furthermore, load forecasting contributes to reducing the generation cost, and it is fundamental to the reliability of the power systems. On the other hand, short-term load forecasting is substantial for economic running. The forecasting precision directly affects the reliability, economy running and supplying power quality of the power system. Hence, finding the required load forecasting method to enhance the accuracy is valuable for forecasting precision. This paper proposed particle swarm optimization (PSO) to improve working support vector machine (SVM), SVM regression model is derived; also derived SVM with PSO. Support vector machine (SVM) model is adopted with and without PSO based on the historical load data and meteorological data of Tajikistan country, analysis the various factors affecting the forecast. The historical data and the load forecasting factors to be considered are normalized. The two parameters of SVM significantly influenced the model, and therefore it optimized using evolutionary algorithm.</p>

Implications of environmental constraints in hydropower scheduling for a power system with limited grid and reserve capacity

AbstractThe negative impacts of power systems on biodiversity have to be mitigated, while simultaneously ensuring affordable and secure electricity supply for the future. This may lead to trade-off situations where ecological, recreational or social needs are weighted against the need for flexible power supply. This paper explores the interaction between the security of electricity supply and environmental constraints on the operation of flexible hydropower plants in the Norwegian renewable-based power system. A long-term, stochastic scheduling model of a wind- and hydropower-dominated power system is used to assess the implications of environmental constraints and reserve capacity requirements in combination. The model is used for a representative case study where three types of environmental constraints are imposed on the operation of the hydropower plants in a region of the congested Norwegian power system. In addition, requirements for spinning and non-spinning reserve capacity have to be met. The case study results demonstrate varying impacts on the operation of the hydropower plants, curtailment of demand and provision of reserve capacity depending on the type of environmental constraint being imposed.

Low-carbon economic scheduling strategy for active distribution network considering carbon emissions trading and source-load side uncertainty

The double uncertainty of the source-load side poses a new challenge to the reliability of the low carbon scheduling in the active distribution network (ADN) system. In this study, a low-carbon economic scheduling model of the ADN system is proposed, which considers step-type carbon emissions trading (CET) mechanism and source-load side uncertainty. The multi-dimensional discrete probability sequences of source-load side are converted into the global equivalent load (EL) by the proposed discretized step transformation (DST) and the convolutional sequence operation (CSO) method, and the predicted value of EL is expressed by the expected value of the EL probability sequence. By setting the probability constraint of the spinning reserve capacity, the influence of power prediction error on the ADN system is fully considered. On this basis, the chance constraint problem (CCP) is further converted to a deterministic mixed-integer linear programming (MILP) problem by linearizing power flow constraints. The proposed strategy is verified based on the IEEE 33-bus ADN system. The experimental results indicate that the system can further realize the synergy of economic benefits and environmental benefits. In addition, compared with other intelligent algorithms, the solution time of the proposed strategy is significantly reduced, and the optimization effect is greatly improved.

Estimating Spinning Reserve Capacity With Extreme Learning Machine Method in Advanced Power Systems Under Ancillary Services Instructions

In advanced power systems, to balance load generation mismatches in the face of unexpected events and to meet unforeseen possibilities, load taking and shedding instructions are required under Ancillary Services (AS), which are performed by providing spinning reserve (SR) in the units. SR is an important factor, which is rather uncertain to predict due to the unpredictability of customers’ consumption, excessive or under-energy generation, and unpredictability in the integration of renewable energy sources. In this study, integration of renewable energy is addressed as a factor for predicting SR. Increasing the integration of solar and wind into the power system requires larger amounts of SR, which creates a significant increase in generation and emission costs. Optimum estimation of SR capacity helps system operators (SO) plan generators in advance and in a better bidding environment. In the past, estimation tools such as feed forward networks and time series models have been used to estimate load and electricity generation. In this article, Extreme Learning Machine (ELM) method is used to estimate SR capacity in the day-ahead and intraday market. The results obtained with ELM were also compared with the results observed with ANN, LR and SVM methods.

Investigation of Optimum Reserve Capacity Requirement in Ancillary Services with Extreme Learning Machine

In advanced power systems, reserves are used to balance load mismatches in the face of unexpected events and to meet unforeseen contingencies. This reserve used in the units is defined as the spinning reserve (SR), which is realized with the loading and unloading instructions within the scope of the Ancillary Services (AS). As the contribution of solar and wind turbines increases in installed power, it requires larger amounts of SR and creates a significant increase in production costs. This article uses the Extreme Learning Machine (ELM) method to estimate SR capacity in the day-ahead and intraday market. In this study, experiments were conducted to determine the effectiveness of ELM-SR in estimating the SR parameter, by randomly dividing the data set into 60–40% training-test sets and using 1–100 neurons in the latent layer. The results obtained with the ELM were also compared with the results observed with the ANN, LR, and SVM methods, which determine the optimum reserve amount of the method used. Thus, even in the face of increased production in renewable energy in the power system with a properly planned reserve with ELM, a significant economic gain is achieved with the allocation of the optimum reserve.

Accelerating Efficient Installation and Optimization of Battery Energy Storage System Operations Onboard Vessels

Emerging large battery energy storage systems (BESSs) are key enablers in the electrification of the shipping sector. With huge government investments in BESSs, there are large gaps between the government supported BESS initiatives and actual BESS integration results on vessels. This study aims to close these gaps, allowing BESSs to become the preferred solution for ship owners without needing government support. Firstly, this industry-driven study reviews both the industrial approaches to achieve CO2 emission reductions and the fuel savings and emission reductions from 500 BESS installations on various vessels. Secondly, a 630 kWh BESS retrofitted onto a hybrid-electric vessel is used to quantitively identify the improvement requirements for installations and operations. The installations required many custom designs that were expensive and have high failure risks. The standardization of interfaces’ between BESSs and vessels is thus urgently required. The BESS was intended for spinning reserve capacity and peak shaving but in practice was under-used in terms of energy throughput (shallow cycles and low equivalent full cycles of 80 versus the design specification of 480 yearly). Thirdly, this study develops new, integrated BESS operational models by learning from large operational data, balancing BESS degradation against fuel saving and utilizing onshore/offshore green power supply/charging. The R&D of BESS is required to deal with the increasing safety requirements and further CO2 emission reductions. Finally, four BESS acceleration scenarios were established to facilitate the technical and operational transferability through utilizing digitalization.

Day-ahead multi-objective optimal operation of Wind–PV–Pumped Storage hybrid system considering carbon emissions

It is crucial to alleviate the problems of energy consumption and grid fluctuations caused by the randomness and intermittency of variable renewable energy (VRE) such as wind power and photovoltaic (PV). Under the new situation of “carbon neutrality”, the optimal operation of Wind–PV–Pumped Storage (PS) hybrid system is studied in this paper. The direct economic benefits, carbon emissions and the stability of active power connected to the grid are selected as the objectives of the hybrid operation system. The Wind–PV–PS optimal operation model is established with the constraints of unit spinning reserve and reservoir capacity with the characteristics of hydropower system. The optimization is executed through Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and the optimal solution is selected by the membership normalization method from the Pareto optimal solution set. The results of the objective values before and after the configuration of PS system show that the stability of power grid decreases with the increase of economic and environmental benefits. The VRE curtailment rate is reduced by 8.57%; the economic rate of return is increased by 14.37%; the carbon emissions are reduced by 3.984 million t/year; and the active power volatility of the grid is reduced as well. The hybrid system with PS plays an important role in promoting the safe, economic and efficient grid connection of VRE.

Partitioning method of reserve capacity based on spectral clustering considering wind power

Abstract With the rapid development of the power system, the complexity of power system continues to increase. The large-scale of clean energy increases the complexity of the power system structure, which makes the power system’s operating status complex and changeable. To ensure the safety, stability, and economic operation of the power system, the power system needs to maintain a certain spinning reserve capacity and ensure that the reserve is available. The spinning reserve optimization method based on fixed partitions cannot effectively deal with the source-net-load volatility and the line congestion problems caused by wind power. To this aim, this paper proposes a power system reserve capacity partitioning algorithm based on the spectral clustering algorithm considering wind power. First, the energy-reserve joint optimization unit commitment problem is built. Secondly, considering the uncertainty of wind power output and the line N − 1 failure, a risk assessment method for line congestion is established; then the power transfer distribution factor (PTDF) of the congested line is considered as the similarity measure. A method based on the iterative partition method based on spectral clustering is proposed. Finally, the reserve capacity configuration based on the dynamic partition results is determined. The IEEE-118 bus system is used in this paper to verify the proposed method. The results show that the reserve capacity dynamic partition method proposed in this paper determines the number of partitions automatically. Reserve capacity partitions are built reasonably. After the reserve capacity partitions, the availability of spinning reserve capacity is effectively guaranteed.

Resilience-oriented operation of power systems: Hierarchical partitioning-based approach

As an achievement of innovations resulting from partitioning mechanisms, these mechanisms can contribute to the more flexible operation of power systems in local communities. The ever-increasing frequency and severity of unexpected real-time failures have created challenges for partitioned-based power system operators, affecting each partition’s resiliency. With this in mind, this paper presents an adaptive local operation strategy (ALOS) for resilient scheduling of the renewable-dominated partitioned-based power systems under normal and islanding modes in a decentralized manner. The main objective of the developed ALOS lies in reaching an affordable preparedness level in each partition to deal with unscheduled islanding mode, which can occur subsequent to real-time failures at common lines between adjacent partitions on transmission level. To this end, a set of resilience-target constraints is presented to prepare sufficient spinning reserve capacity in each partition to ensure continuity of supply during islanding mode. The proposed strategy is formulated as a two-stage stochastic mixed-integer linear program (MILP), and the nested formation algorithm is employed to execute it in a hierarchical fashion based on the privacy-preserving protocols. Besides, the tri-state compressed air energy storage (CAES) system is also included in the proposed strategy to mitigate the negative consequences caused by real-time failures and uncertain sources. Numerical results conducted on the IEEE 30-bus test system reveal that the proposed ALOS can enhance the resilience of each partition in responding to unscheduled islanding mode by efficiently utilizing all available capacities on the generation side. Furthermore, the DIgSILENT PowerFactory is used to identify the worst possible series of events and to evaluate the effectiveness of the proposed resilience-promoting proactive strategy in dealing with these events.

Analysis of Power Supply and Demand in Maharashtra State for Load Forecasting Using ANN

The power system energy management system plays a crucial role in load forecasting. Load forecasting lowers production costs, increases spinning reserve capacity, and improves power system reliability. Financial institutions, power suppliers, and other participants in the electric energy market, such as transmission, generation, and distribution, rely heavily on load forecasts. The allocation of generation for economic reasons is a critical goal of short-term load forecasting. For short-term load forecasting, this research provides a solution paradigm based on an artificial neural network. Dry bulb temperature, Dew point temperature, humidity, and load data are the inputs used to forecast the load. To minimize the error function generated from computed and actual load, the back propagation algorithm was implemented.

A Comprehensive Review of Control Strategies and Optimization Methods for Individual and Community Microgrids

Community Microgrid offers effective energy harvesting from distributed energy resources and efficient energy consumption by employing an energy management system (EMS). Therefore, the collaborative microgrids are essentially required to apply an EMS, underlying an operative control strategy in order to provide an efficient system. An EMS is apt to optimize the operation of microgrids from several points of view. Optimal production planning, optimal demand-side management, fuel and emission constraints, the revenue of trading spinning and non-spinning reserve capacity can effectively be managed by EMS. Consequently, the importance of optimization is explicit in microgrid applications. In this paper, the most common control strategies in the microgrid community with potential pros and cons are analyzed. Moreover, a comprehensive review of single objective and multi-objective optimization methods is performed by considering the practical and technical constraints, uncertainty, and intermittency of renewable energies sources. The Pareto-optimal solution as the most popular multi-objective optimization approach is investigated for the advanced optimization algorithms. Eventually, feature selection and neural network-based clustering algorithms in order to analyze the Pareto-optimal set are introduced.

Day-ahead combined economic and emission dispatch with spinning reserve consideration using moth swarm algorithm for a data centre load

Dynamic combined economic and emission dispatch is an important task in the power system that examines the optimal allocation of power generation resources that yield the least possible fuel and emission costs. In this study, the Moth Swarm Algorithm has been proposed for solving the combined economic and emission dispatch problem for a 24-hour period. The model has been implemented on a test system made up of a combination of thermal and solar photovoltaic plants, while also considering spinning reserve allocation. The results obtained are presented in comparison with commonly used state-of-the-art methods like Moth Flame Optimization, Whale Optimization Algorithm, Ant Lion Optimizer, and Tunicate Swarm Algorithm. Two test systems have been considered in the model implementation. The first system consists of a combination of six thermal plants and thirteen solar plants whose load demand is the hourly energy demand of an anonymous data centre in South Africa. The second test system is made up of three thermal plants and thirteen solar plants to service its unique hourly load demand. Results showed that the proposed MSA gave preference to solar photovoltaic generation over thermal generation given that environmental impact minimization is a major component of the overall objective function. The proposed MSA also scheduled thermal generators to provide the required spinning reserve capacity since solar energy is intermittent in nature. Overall, analyses show that the proposed MSA outperformed the other methods in finding the best fuel, emission, solar generation, and spinning reserve costs that best serve the energy demand of the data centre and the second test system for the 24-hour period.

Short-Term Hydro Generation Scheduling of the Three Gorges Hydropower Station Using Improver Binary-coded Whale Optimization Algorithm

The short-term hydropower generation scheduling (STHGS) is a complicated problem in the utilization of hydropower and water resources. An improved binary-coded whale optimization algorithm (IBWOA) is proposed in this paper to solve the complex nonlinear problem. The STHGS problem is divided into unit combination (UC) subproblem and economic load distribution (ELD) subproblem. For the UC subproblem, we use the sigmoid function (SF) to generate a binary array representing the start/stop state of the unit. The whale algorithm's search mechanism is optimized, and the inertia weight and perturbation variation strategy are introduced to improve the algorithm's optimization ability. Each generation solution is optimized by repairing the minimum uptime/downtime constraint and the spinning reserve capacity constraint. For ELD subproblem, the optimal stable load distribution table (OSLDT) is used to distribute the load quickly. The Mutation mechanism and the Locally balanced dynamic search mechanism compensate for the non-convex problems caused by start-stop constraints and stable optimal table methods. Finally, the proposal is applied to solve the STHGS of the Three Gorges Hydropower Station. When the water head is 75 m,88 m, and 107 m, the minimum water consumption calculated by the IBWOA algorithm is 1,058,323,464 m3, 892,524,696 m3, and 745,272,216 m3, respectively. Compared with the traditional whale optimization algorithm, the water consumption of the IBOWA algorithm corresponding to 75 m, 88 m, and 107 m water heads is reduced by 0.76%, 0.26%, and 0.05%, respectively. The comparison between the IBWOA algorithm and other heuristic algorithms shows that the IBWOA has good feasibility and high optimization accuracy.

Assessing the benefits of exchanging spinning reserve capacity within the hydro-Dominated nordic market

This paper presents a hydrothermal scheduling toolchain suitable for detailed studies of procurement of spinning reserves in the hydro-dominated Nordic power system. The toolchain combines a long-term model to find the expected marginal value of water in the hydropower reservoirs and initial states for thermal generators, and a short-term model to optimize the daily unit commitment and dispatch. The short-term model has a detailed description of both hydro and thermal generation technologies to realistically constrain their capabilities as reserve capacity providers. The toolchain is applied on a data description representing a 2030 scenario of the Northern European power system to quantify the benefits of exchanging spinning reserve capacity both between bidding zones and between countries within the Nordic market. By allowing 10% of the transmission line capacity for exchange of reserves, we find that the daily average economic benefit is 290 k€ and 102 k€ for reserve exchange between bidding zones and countries, respectively. Moreover, we quantify and illustrate the importance of applying unit commitment for hydropower stations and strictly enforcing their minimum power requirements.

An Effective Algorithm for MAED Problems with a New Reliability Model at the Microgrid

This paper proposes a new framework for multi-area economic dispatch (MAED) in which the cost associated with the reliability consideration is taken into account together with the common operational and emission costs using expected energy not supplied (EENS) index. To improve the reliability level, the spinning reserve capacity is considered in the model as well. Furthermore, the MAED optimization problem and non-smooth cost functions are taken into account as well as other technical limitations such as tie-line capacity restriction, ramp rate limits, and prohibited operating zones at the microgrid. Considering all the above practical issues increases the complexity in terms of optimization, which, in turn, necessitates the use of a powerful optimization tool. A new successful algorithm inspired by phasor theory in mathematics, called phasor particle swarm optimization (PPSO), is used in this paper to address this problem. In PPSO, the particles’ update rules are driven by phase angles to essentially ensure a spread of variants across the population so that exploitation and exploration can be balanced. The optimal results obtained via simulations confirmed the capability of the proposed PPSO algorithm to find suitable optimal solutions for the proposed model.

Probabilistic estimation of spinning reserves in smart grids with Bayesian-driven reserve allocation adjustment algorithm

Purpose This study aims to introduce a methodology for optimal allocation of spinning reserves taking into account load, wind and solar generation by application of the univariate and bivariate parametric models, conventional intra and inter-zonal spinning reserve capacity as well as demand response through utilization of capacity outage probability tables and the equivalent assisting unit approach. Design/methodology/approach The method uses a novel approach to model wind power generation using the bivariate Farlie–Gumbel–Morgenstern probability density function (PDF). The study also uses the Bayesian network (BN) algorithm to perform the adjustment of spinning reserve allocation, based on the actual unit commitment of the previous hours. Findings The results show that the utilization of bivariate wind prediction model along with reserve allocation adjustment algorithm improve reliability of the power grid by 2.66% and reduce the total system operating costs by 1.12%. Originality/value The method uses a novel approach to model wind power generation using the bivariate Farlie–Gumbel–Morgenstern PDF. The study also uses the BN algorithm to perform the adjustment of spinning reserve allocation, based on the actual unit commitment of the previous hours.

A Joint Optimization Model for Energy and Reserve Capacity Scheduling With the Integration of Variable Energy Resources

In this paper, a two-stage approach is proposed on a joint dispatch of thermal power generation and variable resources including a storage system. Although, the dispatch of alternate energy along with conventional resources has become increasingly important in the new utility environment. However, recent studies based on the uncertainty and worst-case scenario-oriented robust optimization methodology reveal the perplexities associated with renewable energy sources (RES). First, the load demand is predicted through a convolutional neural network (CNN) by taking the ISO-NECA hourly real-time data. Then, the joint dispatch of energy and spinning reserve capacity is performed with the integration of RES and battery storage system (BSS) to satisfy the predicted load demand. In addition, the generation system is penalized with a cost factor against load not served for the amount of energy demand which is not fulfilled due to generation constraints. Meanwhile, due to ramping of thermal units, the available surplus power will be stored in the backup energy storage system considering the state of charge of the storage system. The proposed method is applied on the IEEE-standard 6-Bus system and particle swarm optimization (PSO) algorithm is used to solve the cost minimization objective function. Finally, the proposed system performance has been verified along with the reliability during two worst-case scenarios, i.e., sudden drop in power demand and a short-fall at the generation end.

Research on calculation of spinning reserve capacity of wind power system considering multiple uncertainties

With the development of artificial intelligence technology, power system has gradually adopted this technology. In order to better promote the application of artificial intelligence in power system, various algorithms for power system need to be further optimized and improved.After a large-scale wind power grid-connected, its random fluctuation characteristics significantly increase the uncertainty of power system operation. The traditional deterministic reserve capacity determination method has been difficult to meet the needs of the safe and economic operation of the system. It is necessary to comprehensively consider all kinds of uncertain factors that may change the operation state of the system, so as to accurately obtain the rotational reserve demand of the system. Based on this, considering the multiple uncertainties of wind power forecasting error, load forecasting error and conventional outage, this paper proposes a method to determine the reserve capacity of power system including wind power, and deduces the quantitative relationship between upper reserve capacity and loss of load probability (LOLP), lower reserve capacity and wind curtailment probability (WCP). Finally, a case study is given. The research in this paper can be used as the basis of coordinated dispatching and reserve sharing of interconnected power systems.