Spinning Reserve Research Articles

A two-layer strategy for sustainable energy management of microgrid clusters with embedded energy storage system and demand-side flexibility provision

The intermittent nature of renewable energy generation and the fluctuating demands pose persistent challenges in microgrid operations. In response, stakeholders and operators have turned to clustering the geographically adjacent microgrids as a solution. In this context, this paper introduces a novel two-layer energy management strategy for microgrid clusters, utilizing demand-side flexibility and the capabilities of shared battery energy storage (SBES) to minimize operational costs and emissions, while ensuring a spinning reserve within individual microgrids to prevent load-shedding. In the lower layer, the proposed approach devises optimal day-ahead operation policies, while the upper layer employs a cooperative strategy to further optimize the operational efficiency across the entire cluster. The energy management problem is accurately formulated as a mixed integer quadratic programming (MIQP) optimization, which incorporates linear terms in the problem's constraints. The formulation accounts for operational costs associated with SBES including expenses of charging/discharging and changes in operating states (CiOS). Real-world case studies with a cluster of three microgrids in Australia validate the effectiveness of this approach. Results show a reduction in operational costs for the base case scenario by 6.96 % compared to conventional microgrid management strategies. Sensitivity analyses further demonstrate the economic benefits of varying SBES capacity and flexibility pricing, with savings ranging from 6.5 % to 8.1 %. The proposed strategy also reduces CO2 emissions by up to 11.6 %, while improving system reliability. This strategy holds promise for integration into distributed energy systems with high renewable penetration and clustered local grids, offering significant advantages for utility operators and end-users through improved energy efficiency and reduced emissions.

A novel algorithm for optimizing genset operations to minimize fuel consumption in remote diesel-RES microgrids

This paper addresses the challenge of reducing fuel consumption in Diesel-RES (Renewable Energy Sources) isolated microgrids, particularly focusing on Diesel Genset’s (DG) operation. The study introduces a basic rule based energy management system that serves as a platform to test out various DG operational strategies with a novel approach. Two optimization strategies—load dispatch optimization and unit commitment optimization—are explored to unequally distribute loads among different grid-connected DGs and sequence their start/stop based on predictive demand profiles respectively. Additionally, the integration of a spinning reserve-providing battery is investigated to alleviate DGs from their spinning reserve constraint, resulting in higher operational loads and consequently higher efficiency. The proposed model is applied on a case study of the Tahitian power system, demonstrating reductions in fuel consumption. The combined application of the proposed DG load dispatch and unit commitment optimizations, along with the integration a spinning-reserve-providing battery, yielded a 2.6 % reduction in fuel consumption and 6kt decrease in CO2 emissions over a year compared to a basic DG operation without a battery.

Optimal solution of multiobjective stable environmental economic power dispatch problem considering probabilistic wind and solar PV generation

The Environmental Economic Power Dispatch (EEPD) problem, a widely studied bi-objective nonlinear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This paper addresses these concerns by formulating and solving the Stable Environmental Economic Power Dispatch (SEEPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modeled using random variable generation techniques, applying Gaussian, Weibull, and log-normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic SEEPD problem extends to multiple periods by replicating the single-period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. Multi-Objective Evolutionary Algorithms (MOEAs) have gained importance for solving complex nonlinear problems involving multi-objective functions. This paper applies the latest MOEAs to tackle the proposed SEEPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp-up and ramp-down constraints for thermal generators. A bidirectional coevolutionary-based multi-objective evolutionary algorithm is employed, integrating an advanced constraint-handling technique to ensure compliance with system constraints. The simulation results show that the proposed formulation achieves a better trade-off between various conflicting objective functions compared to other state-of-the-art MOEAs.

A decision-making model for joint energy and reserve scheduling of wind power producers with local intraday demand response exchange market

In this paper, a three-stage stochastic bi-level optimization framework is presented for optimal participation of wind power producers (WPPs) in day-ahead (DA), intraday, and balancing markets. In this framework, to leverage demand response (DR) services, a peer-to-peer (P2P) energy trading platform is implemented that allows local load aggregators (LAs) to contribute to the intraday markets improving both LAs’ and WPP’s benefits. Participating in the intraday DR exchange (IDRX) market enables WPP to purchase DR services from LAs, to reduce the penalty cost on the deviation between the day-head bidding and the real-time dispatch. A Stackelberg game for the bi-level decision-making model captures the conflict of interests between the WPP and LAs, in which, the upper level seeks to maximize WPP profit, while the lower level aims to maximize LAs’ economic surplus. The bi-level model is converted into its equivalent single-level mixed-integer quadratic problem (MIQP) employing the Karush-Kuhn-Tucker (KKT) conditions and strong duality theorem. Simulation results show that participation of the WPP in the IDRX market and employing spinning reserve and DR services for compensating the uncertainties are greatly dependent on its risk preferences and increase its expected profit in all conditions, significantly.

Addressing the limitation of Operational Flexibility in Power Generation with Pumped Hydro Energy Storage System – Indian Perspective

The global climate change scenario has impacted and taken corrective measures in shifting the sourcing of primary energy and efficient end-use pattern with more focus to utilise renewable energy resources to replace the depleting fossil fuels. Electric power generation capacity in the Indian subcontinent has gradually been improved to substantially contribute to intermittent and variable renewable power generation mix up to 40% of grid demand. However, the injection of grid-integrated renewable power as per the availability of generation potential will necessitate limiting the power generation from generating plants running on fossil fuel. But the operational flexibility of fossil fuel power plants is mostly designed to run efficiently with a 55% minimum technical load on the machines. In the above situation, stable grid availability and operation with a desirable spinning reserve may be taken care of suitably with an on-grid energy storage system, and in the Indian context, a pumped hydro energy storage system may play a critical role to take care of grid operational variability with adequate measures on frequency and voltage correction through periodic power generation and power draw. In this paper, the feasibility of a pumped hydro storage system from an Indian power sector perspective has been reviewed about the present national power scenario.

Multi‐objective multiperiod stable environmental economic power dispatch considering probabilistic wind and solar PV generation

AbstractThe economic‐environmental power dispatch (EEPD) problem, a widely studied bi‐objective non‐linear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This article addresses these concerns by formulating and solving the economic environmental and stable power dispatch (EESPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modelled using random variable generation techniques, applying Gaussian, Weibull, and log‐normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic EESPD problem extends to multiple periods by replicating the single‐period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. Multi‐objective evolutionary algorithms (MOEAs) have gained prominence for solving complex non‐linear problems involving multi‐objective functions. This article applies the latest MOEAs to tackle the proposed EESPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp‐up and ramp‐down constraints for thermal generators. A bidirectional coevolutionary‐based multi‐objective evolutionary algorithm is employed, integrating an advanced constraint‐handling technique to ensure compliance with system constraints. The simulation results show that the proposed formulation achieves a better trade‐off between various conflicting objective functions compared to other state‐of‐the‐art MOEAs.

Source-Storage-Load Flexible Scheduling Strategy Considering Characteristics Complementary of Hydrogen Storage System and Flexible Carbon Capture System

In the current literature, there exists a lack of analysis regarding the coordination of the spinning reserve and time-shift characteristics of hydrogen storage systems (HSS) and flexible carbon capture systems (FCCS) in terms of low-carbon economic operation. They are presently used solely as a tool to capture carbon dioxide, without fully utilizing the advantages of their flexible operation. The coordination and complementarity of the FCCS and HSS can ensure stable power supply and improve renewable energy (RE) consumption. Combined with demand side response (DSR), these factors can maximize the RE consumption capacity, reduce carbon emissions, and improve revenue. In this paper, a source-storage-load flexible scheduling strategy is proposed by considering the complementary nature of FCCS and HSS in terms of rotating standby and time-shift characteristics. First, the operational mechanisms of FCCS, HSS, and demand side response (DSR) are analyzed, and their mathematical models are constructed to improve flexibility in grid operation and regulation. Next, deficiencies in FCCS and HSS operation under rotating reserve requirements are analyzed to design a coordinated operation framework for the FCCS and HSS. This operational framework aims to enable the complementarity of the rotating reserve and time-shift characteristics of FCCS and HSS. Finally, based on the carbon emission trading mechanism, a three-stage ladder carbon emission trading cost model is constructed, and a source-storage-load flexible scheduling strategy is established to achieve an effective balance between low carbon emissions and economic performance. The simulation results demonstrate that the strategy reduces the overall cost by 8.57%, reduces the carbon emissions by 35.33%, and improves the renewable energy consumption by 3.5% compared with the unoptimized scheme.

Co-optimization of power and spinning reserve in multi-area electrical networks with wind farms

Integrating wind farms into electrical networks reconfigures energy matrices and poses new challenges to system operators. For instance, wind farm owners must now guarantee spinning reserves stipulated in grid codes. This prompts an urgent quest for optimizing power and reserve allocation in liberalized markets using advanced models. In this sense, this paper introduces an optimal power flow (OPF) model to co-optimize power and spinning reserve in multi-area electrical networks with wind farms. The granularity and layout of the wind plant are considered for a correct calculation of the spinning reserve in each variable speed wind turbine. Unlike classical OPF studies, where generator participation is free and mandatory in system reserves, this new approach considers both power and reserve as variable factors in the optimization process, which depend on reserve costs, system load, and wind speed. The approach is evaluated through comparative analyzes on two power networks: a small 12-bus, two-area network with two wind farms and a larger 100-bus, four-area network with four wind farms. Multi-period 12-hour look-ahead analyzes are conducted with variable conditions of demand and wind speed. The studies demonstrate the advantages of the new model compared to the classical OPF, showcasing its ability to optimize power dispatch with wind farms actively contributing to the spinning reserve requirements of the power network.

Security constrained economic dispatch with VSC-HVDC connected wind farms

The rise in wind power in electrical networks creates complex challenges for transmission system operators (TSOs), particularly regarding real-time operation. TSOs rely on grid codes to enforce wind farm (WF) participation in primary frequency control (PFC), like conventional plants. This paper addresses this need by proposing a security constrained economic dispatch (SCED) model with VSC-HVDC connected WFs based on variable speed wind generators. A key differentiator from existing models is the explicit calculation of the spinning reserve and the droop parameter of the WFs. This method uses the deloading schemes related to blade pitch angle and mechanical rotor speed to ensure compliance with grid codes related to PFC. We show that the wind speed and these deloading schemes significantly impact the WF droop parameter. For efficient studies, linear shift factors are used to model the transmission grid and the VSC-HVDC connected WFs. The performance of the SCED model was assessed using two standard test systems that incorporate WFs connected by VSC-HVDC lines. Its effectiveness was confirmed by comparing it with the security constrained optimal power flow (SCOPF). The results showed agreement between both models, with differences less than 3 % and with the SCED saving about 70 % of the computing time.

An integrated skip convolutional network with residual learning and feature extraction for point and interval prediction of solar radiation

Spinning reserve based on solar radiation prediction can ensure the secure operation of large-scale grid-connected photovoltaic systems, but irrational spinning reserve can lead to substantial economic losses and even grid collapse. Therefore, it is imperative to identify characteristic patterns of solar radiation and establish an effective solar radiation prediction model. However, existing hybrid models often struggle to efficiently recognize and leverage input features, compromising the robustness of the model. To address this limitation, this study proposes an integrated skip-convolutional network with residual learning and feature extraction (InSCNet), which enhances the capability to represent information data and thereby improves the stability and accuracy of solar radiation prediction by employing a series of feature extraction and prediction blocks with residual learning. InSCNet includes a sophisticated feature extraction module to effectively address the underutilization of feature information, and it incorporates residual learning, skip-convolution, and recursive networks in the prediction module, reducing the risk of gradient vanishing or gradient explosion while enhancing prediction accuracy. Additionally, an interval estimation method with adaptively chosen distributions is introduced, which extends the prediction interval estimation method. The proposed InSCNet is rigorously evaluated using datasets from three major cities in Pakistan. Experimental results demonstrate that InSCNet outperforms existing solutions in both point and interval predictions.

A two-stage optimal mechanism for managing energy and ancillary services markets in renewable-based transmission and distribution networks by participating electric vehicle and demand response aggregators

The growing uncertainties in power operations due to the integration of renewable generations (RGs) and electric vehicles (EVs) into electricity grids have amplified the significance of ancillary services (AS). These services have become essential to ensure the sustainable functioning of the grid. In light of this, we introduce a two-stage optimization framework to manage competitive energy and AS markets at the interface of the transmission system (TS) and distribution system (DS). Our approach takes into account a comprehensive set of economic, technical, and security factors. This mechanism is structured in two stages: the first stage encompasses the energy market, while the second stage encompasses AS markets. Spinning reserve (SR) is supplied by conventional thermal units (TUs), whereas regulation capacity is provided by energy storage systems (ESSs), fast-response generators, electric vehicles (EVs), and demand response (DR) aggregators. We applied this mechanism to a 30-bus transmission network connected to four 10-node DSs and utilized the GUROBI solver in GAMS for solving. The simulation results demonstrate that the engagement of DSs in the SR market reduces the reliance on costly TUs, thereby decreasing system costs by approximately 10%. Furthermore, involving ESSs, EVs, and DR aggregators in the regulation market enhances technical performance and results in a 6.91% reduction in total system costs. This approach provides a robust solution to the evolving challenges posed by RGs and EVs in modern electricity grids.

Wind power deviation charge reduction using long short term memory network

High penetration of variable generation like wind in modern power systems results in frequent load-generation imbalances. Additional spinning reserves and balancing services are deployed to reduce or avoid such imbalances. However, these additional balancing services result in increased total operation costs and it is generally shifted to Wind Power Generation Companies (WPGC) as Deviation Charges (DCs). The high DCs diminish the profitability of WPGCs. DCs are directly proportional to forecasting errors and can be reduced by enhancing forecasting accuracy. This paper proposes a Long Short-Term Memory (LSTM) network to reduce DCs. LSTM is a special kind of recurrent neural network and it is capable of learning long-term and short-term dependencies effectively. Therefore, the LSTM network can produce minimal error wind power forecasts and this helps to reduce DCs. The performance of the proposed LSTM network is realized using the real system data. Results obtained from the proposed LSTM model are compared with various forecasting models like ARIMA, ANN, and SVR. The comparison shows that the proposed LSTM model shows the least total DCs on most of the days followed by ANN, SVR, and ARIMA. The proposed LSTM, ANN, SVR, and ARIMA models show an average total DC of 6194.25$, 8459.32$, 15822.22$, and 38269.89$ respectively. Thus, the proposed LSTM model is capable of reducing 517.82%, 155.43%, and 36.56% DCs from ARIMA, SVR, and ANN models respectively, and helps the WPGCs to enhance their profitability.

Transmission expansion planning in a wind-dominated power system: A closed-loop approach

The current paradigm for the transmission expansion planning (TEP) problem follows an open-loop (OL) approach. This approach first executes an accuracy-oriented forecasting method to predict the available wind power, demand, and spinning and non-spinning reserve (SR and NSR) requirements. Afterward, the TEP problem is solved based on these predictions. However, this OL model does not necessarily obtain a better expansion plan in terms of the real system cost (RSC) against the actual realization of wind and demand. This article contributes to the existing literature by developing a closed-loop (CL) strategy for the TEP problem using a cost-oriented prediction method. In this novel cost-oriented methodology, the RSC assesses the forecasting data quality rather than the statistical indices (accuracy-oriented). In this regard, a bilevel programming model is established to train the predicted data to minimize the system RSC. The upper level trains the predictors for wind power and SR/NSR requirements, while the two lower-level problems formulate the unit commitment (UC)-based TEP and the economic redispatch (ERD) problem. The bilevel programming that contains integer variables at both levels is solved by the reformulation and decomposition (R&D) technique. The numerical results on the IEEE 118-bus grid illustrate the cost-effective advantages of the CL-TEP method.

Analysis of power dispatching decisions with energy storage systems using the optimal probability distribution model of renewable energy

Effectively managing the inherent unpredictability and fluctuation attributes of renewable energy sources within scheduling systems has emerged as a critical matter demanding immediate attention. The incorporation of energy storage technology offers notable advantages by mitigating fluctuations in wind power generation and curtailing peak shaving costs in scheduling systems through economical utilization of energy storage. This study employs the adjustment of energy storage’s reserve capacity function to modify the economic gains and losses arising from fixed spinning reserve. This approach tailors the reserve capacity function by formulating the uncertainty inherent in wind power generation. The paper presents an enhanced selection method for calculating optimal bandwidth within the framework of least squares approximation probability density function scenarios. The fusion of least squares approximation and optimal bandwidth computation permits precise refinement of bandwidth in non-parametric kernel density estimation approaches. Consequently, the study puts forth a comprehensive model for coordinated scheduling and regulation of a multi-scale energy storage power system. Finally, the stability and economy of the proposed method in the scheduling operation process were verified through simulation, and the research results can provide a theoretical basis for future uncertain wind power scheduling.

Improved wind forecasting with wavelets

An important consequence of a high wind penetration and the difficulty to predict the power generated is the need to entrust the coverage of a large power variations to power spinning reserve. A forecasting system of generation would resolve, at least in part, this problem. The wind variability has a significant negative influence on the forecast models. To avoid the influence of variability, we applied a wavelet filter that can work with softer signals. The results improve significantly when wavelet filtering is applied.

Distributed Control Strategy for Isolated Electrical Hybrid Power Systems

This paper presents a distributed control for isolated electrical hybrid power systems to manage the energy generated by the elements of the microgrid. Unlike other control strategies seen in the literature, this distributed control strategy takes into account the dynamic behavior of the synchronous generators and the spinning reserve requirements. The distributed control offers a simpler implementation than other proposals, a better integration of renewable generation, a greater reliability against communication failures and it reduces the energy costs. The main objectives of the controllers are minimizing fuel consumption and maximizing renewable generation. Simulation results are obtained using MATLAB/Simulink to verify the effectiveness of the proposed control strategy.

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.

A Multitask Integrated Deep-Learning Probabilistic Prediction for Load Forecasting

Spinning reserve without accurate load forecasting can lead to automatic disconnection of critical loads by under-frequency load shedding devices. Such a predicament poses a grave threat to the economic and social welfare of the affected region, and in extreme scenarios, can result in a debilitating grid collapse. However, existing models lack the deep feature extraction capability and cannot accurately predict the uncertainty of electricity demand. Thus, a multitask integrated deep-learning probabilistic prediction with multidimensional feature extraction based on granularity information and quantile regression is explored in this study to solve the insufficient feature extraction problem in probabilistic prediction. In the proposed scheme, we designed a multi-layer feature extraction framework, which information granularity based on fuzzy rough sets to extract time-domain features, multilayer Laplace operators to extract features globally, and quantile regression recurrent neural network variants to extract time context information. In addition, the Pareto integration method is extended to capture better individuals. The experimental results demonstrate that the proposed system can effectively improve deterministic forecasting and uncertainty analysis of electricity demand and manage daily fluctuations.

Multi-objective price based flexible reserve scheduling of virtual power plant

In this research, multi-objective optimization of VPP is performed by considering multiple renewables and co-generation sources and solved by using the recently developed multi-objective Harris Hawk’s optimization (MOHHO) algorithm. Renewable sources comprise solar Photovoltaic (PV), wind power, fuel cells along with energy storage systems (ESS), electric vehicles (EV), along with CHP units are associated to form a VPP system. The penalty factor approach is implemented along with the weighting factor to develop a multi-objective framework that can simultaneously capitalize on the net profit and minimalize the emission while taking proper care of the related constraints. In this research, EVs are considered as a flexible reserve option along with a dedicated ESS applicable as a spinning reserve. Also, a dynamic pricing-based strategy is also presented and the optimization of VPP is accomplished by implementing a multi-objective-based day-ahead scheduling (MODAS) to see the behaviour of the anticipated system with a suitable method of constraint handling. Four diverse illustrations are considered in three different case study which is performed for hourly-based scheduling and the results are compared with the proposed technique. Statistical analysis is performed and the quality solution sets are attained by the HHO algorithm after performing 100 independent trials and the same is correlated with the available studies that indicate the efficacy and appropriateness of the selected approach.

Automatic Generation Control Ancillary Service Cost-Allocation Methods Based on Causer-Pays Principle in Electricity Market

The electric power system is rapidly transforming to address the urgent need for decarbonization and combat climate change. Integration of renewable energy sources into the power grid is accelerating, creating new challenges such as intermittency and uncertainty. To address these challenges, this paper proposes a new design of automatic generation control (AGC) ancillary service cost allocation based on the causer-pays rule. The proposed design treats reserves as inventory and aims to minimize them by allocating costs among consumers based on the causative factors for AGC operation. Two cost-allocation methods based on the causer-pays principle are introduced. The first method distributes costs according to the changes in loads causing ancillary service operation, while the second method considers opportunity costs. The case study on the IEEE 39 Bus System demonstrates that the proposed methods incentivize consumers to minimize volatility, resulting in reduced reserve requirements for system operation. In particular, the opportunity cost-based approach encourages loads and variable renewable energy (VRE) to actively reduce volatility, resulting in more efficient power system operation. In conclusion, the novel AGC ancillary service cost allocation methods offer a promising strategy for minimizing spinning reserves, increasing the power system’s efficiency, and incentivizing consumers to actively participate in frequency regulation for a more sustainable and reliable electricity market.