Reserve Scheduling Research Articles

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.

Two-stage coordinated scheduling of hydrogen-integrated multi-energy virtual power plant in joint capacity, energy, and ancillary service markets

With the emergence of distributed resources (DRs), developing an effective, profitable, and green management method is of great importance. Virtual power plant (VPP) can aggregate various DRs, optimize their schedules, and participate in electricity markets uniformly to earn profit. The optimal scheduling problem in spot market has been widely studied, however, few studies have extended the services of VPP to the long-term scale. This paper proposes a coordinated scheduling method for a multi-energy virtual power plant (MEVPP), considering the integration of hydrogen facilities. A holistic market framework, including capacity market, energy market (EM), and ancillary service market (ASM) is developed jointly for the coordination among the annual capacity schedule, daily energy schedule, and daily reserve schedule of MEVPP. A hybrid hydrogen storage system including daily hydrogen storage and seasonal hydrogen storage is modeled to achieve both intraday and cross-seasonal peak shaving. Stochastic programming is used to tackle the uncertainties and risk management is considered by conditional value at risk. With the help of numerical analysis in case studies, it can be concluded that the proposed coordinated scheduling method can improve the economy of MEVPP. The profit from joint market decreases the total cost by about 52 %. The capacity adequacy and flexibility of MEVPP can be improved by the coordination of capacity and reserve services. The hybrid hydrogen storage system can realize the efficient integration of hydrogen and increase hydrogen production and consumption by about 9 %.

DeSSR: A Decentralized, Broadcast-Based Scalable Scheduling Reservation Protocol for 6TiSCH Networks

DeSSR: A Decentralized, Broadcast-Based Scalable Scheduling Reservation Protocol for 6TiSCH Networks

Occupancy schedule development and its effect on OpenStudio prototype college building model

College buildings have unique characteristics compared with school buildings. Therefore, defining the realistic occupancy schedule in a prototype college building has significant research opportunities. In this study, the actual operating schedules of each space type were collected and generated based on the class reservation schedule and compared with the previous reference schedule (primary/secondary school). The schedules were analyzed for their effect on the OpenStudio prototype college building model. The findings highlight that the use of a typical school building schedule in a college building impairs the granularity of information. The analysis shows significant differences between the previous occupancy schedule and the updated occupancy schedule of the college building, leading to a considerable decrease in occupancy density. Furthermore, the effect of these occupancy pattern changes on the prototype building model is examined. The variations were observed in minimum ventilation requirements, the average mechanical ventilation rate, and energy consumption attributed to changes in occupancy density.

Technical indicator enhanced ultra‐short‐term wind power forecasting based on long short‐term memory network combined XGBoost algorithm

AbstractThe growing integration of renewable energy sources into the power grid has introduced unprecedented uncertainty. Ensuring an appropriately scheduled reserve is essential to accommodate renewable energy's intermittent and volatile nature. This study introduces an innovative approach to ultra‐short‐term wind power forecasting, which relies on feature engineering and a hybrid model. The effectiveness of this proposed method is showcased through a case study involving a utility‐scale wind farm in Inner Mongolia, China. The findings indicate that the hybrid model, which combines the XGBoost (Extreme Gradient Boosting) algorithm and LSTM (Long Short‐Term Memory) network with KDJ (Stochastic Oscillator), and MACD (Moving Average Convergence and Divergence), achieves the highest forecasting accuracy. Specifically, the proposed model yields a normalized mean absolute error of 0.0396 for wind power forecasting. The modelling and forecasting process takes approximately 550 s. Furthermore, the suggested method is employed to predict wind power and wind speed for a wind farm in the USA. The experimental results consistently indicate that the proposed model maintains a dependable performance across various raw datasets; it is suitable for use in power system operations.

Performance comparison of dynamically controlled particle swarm optimization and grey wolf optimizer for dynamic power reserve scheduling in multi areas problems

The primary goal of Multi-Area Economic Dispatch (MAED) is to effectively meet the dynamic power demands of multiple interconnected regions while optimizing the economical generation dispatch strategy. This paper introduces a comprehensive investigation into the application of two advanced optimization techniques, namely Dynamically Controlled Particle Swarm Optimization (DCPSO) and thereafter Grey Wolf Optimizer (GWO), and their variant adaptations to address the complex challenges posed by MAED issue. The performance and efficacy of these strategies are rigorously assessed in three distinct scenarios: (a) Single-Area Power System with Three Generation Units: This scenario represents a fundamental single-area power network comprising three generation units. (b) Two-Area System with Four Generating Units: In this case, a two-area system with four generating units is analyzed. (c) Four-Area System with Forty Generation Units and Six Tie Lines. In the most intricate scenario, a four-area interconnected power system is scrutinized, featuring a total of forty generation units. GWO technique produces the cost saving of $ 0.1 for single area, 2 area cost saving is $ 0.11 likewise for 4 area the cost saving is $ 474.10 over the PSO. The execution time taken by GWO is 12.7 sec. less compared to DPSO.

Distributed Energy and Reserve Scheduling in Local Energy Communities Using L-BFGS Optimization

Distributed Energy and Reserve Scheduling in Local Energy Communities Using L-BFGS Optimization

Optimal HVAC Energy and Regulation Reserve Scheduling in Power Markets

This paper considers the optimal scheduling of the hourly energy consumed and regulation reserve capacity offered by a Heating, Ventilation and Air-Conditioning (HVAC) system participating in a day-ahead electricity market. We formulate an Integrated hourly energy and regulation reserve Scheduling and Deployment (ISD) problem using high fidelity models of the HVAC and the building structure's thermal storage property. The ISD problem optimizes the hourly costs and benefits resulting from the power consumed, the regulation reserve capacity offered, the occupant thermal comfort utility, and the expected Intra-Hour Costs (IHCs) due to the HVAC's imperfect tracking of the regulation signal broadcasted by the Independent System Operator every few seconds. Addition of the IHCs to the objective function is the major innovation of this paper. It enables optimal HVAC scheduling to internalize expected regulation signal tracking error cost traded-off against incremental occupant thermal discomfort that may result from perfect tracking. The cost causation circle closes by noting that a high hourly regulation reserve offer may result in higher expected IHCs by increasing the associated tracking error and the incremental occupant discomfort. The paper's innovation is achieved by (i) estimating an analytic function of the hourly HVAC scheduling decisions and state variables that approximates the expected IHCs, and (ii) including this exogenously estimated analytic IHCs function in the objective of the hourly ISD optimization problem. Non-convexities and non-linearities introduced to the HVAC optimization problem resulting from high modeling fidelity and the inclusion of the expected IHCs, are addressed efficiently through piecewise convex relaxations that provide tight optimality bounds. Extensive numerical results are finally provided to demonstrate the applicability and performance of our ISD problem formulation on a realistic office building.

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.

Comparison between plant-based and unit-based production functions for the day-ahead energy and reserve scheduling of a hydropower plant with common waterways

Comparison between plant-based and unit-based production functions for the day-ahead energy and reserve scheduling of a hydropower plant with common waterways

Low-carbon operation constrained Two-stage Stochastic Energy and Reserve Scheduling: A Worst-case Conditional Value-at-Risk approach

Low-carbon operation constrained Two-stage Stochastic Energy and Reserve Scheduling: A Worst-case Conditional Value-at-Risk approach

Stochastic risk-averse energy and reserve scheduling and pricing schemes with non-convexities and revenue caps

Stochastic risk-averse energy and reserve scheduling and pricing schemes with non-convexities and revenue caps

Day-ahead optimal peer-to-peer energy trading strategy for multi-microgrids based on Nash bargaining game with data-driven chance constraints

This paper proposes an optimization model to obtain the day-ahead optimal peer-to-peer (P2P) trading strategy for multi-microgrids (MMG). Firstly, a joint economic energy and reserve scheduling model of a microgrid (MG) is established while considering specific network constraints. Then, to mitigate the impact of renewable energy and load forecasting errors, chance constraints are introduced for the reserve capacity and buses voltage limitations of an individual microgrid. Additionally, a versatile distribution method is adopted to capture the probability distribution of uncertain variables in a data-driven manner, avoiding any prior assumptions. Finally, Nash bargaining theory is employed to deal with the P2P energy trading problem among MMG. The problem is equivalently transformed into two sequential subproblems for solving. Moreover, the alternating direction method of multipliers algorithm is used to solve the subproblems in a distributed manner for privacy concerns. The proposed model not only enables effective P2P energy trading for MMG, but also ensures compliance with the internal network constraints of each MG. Furthermore, the scheduling strategy exhibits robustness in handling forecast errors related to renewable energy and load. In case studies, MMG containing three interconnected MGs is constructed based on the IEEE-123 bus distribution network, and simulation results show that the cost of the MMG is reduced by 9.92 % while all the MGs can benefit from P2P trading. In addition, the risk-averse scheduling results of energy and reserve are obtained, and the conservativeness can be controlled by changing the confidence level, which verifies the effectiveness of the proposed model.

Day-ahead energy-mix proportion for the secure operation of renewable energy-dominated power system

Advancements in various scientific fields have encouraged the development of novel tools, techniques, components, methodologies, and innovations aimed at addressing the challenges encountered in modern power systems dominated by inverter-based resources (IBRs). This paper focuses on a concept that leverages historical time-series data obtained from transmission system operators (TSOs) to enhance the secure management and operation of power systems. By employing a data-driven model, the day-ahead values of power generation and load consumption are estimated and integrated with a dynamic model of the power system for further analysis. To optimize energy generation and ensure grid stability, an energy-mix operation and reserve scheduling model is utilized. This model optimally combines different power-generating technologies, including synchronous generators (SGs), grid-following converters (GFLs), and grid-forming converters (GFMs), to meet the energy demands of the day while enhancing the overall system strength. The findings are supported by quantitative analysis utilizing variables such as frequency, power production, terminal voltages, and system non-synchronous penetration (SNSP). Simulation results demonstrate that implementing the proposed concept enables the power system under consideration to operate securely, even in the face of a 38% increase in immediate load, with a maximum SNSP ratio of 59%. These findings highlight the effectiveness of the proposed approach in addressing the reliability, system dynamics, stability, control efficiency, and security challenges posed by IBR-dominated power systems. Furthermore, it is believed that this research contributes to the ongoing efforts in decarbonization, renewable energy integration, and combating global warming by facilitating the secure and optimized operation of renewable energy-dominated power systems.

Robust low-carbon energy and reserve scheduling considering operational risk and flexibility improvement

With the increase in wind power integration, significant uncertainty leads to enormous requirements for the flexibility and robustness of system operation. To enhance the flexibility of the power system, this paper proposes a robust low-carbon optimization method for energy and reserve scheduling. The system flexibility is improved by various flexible resources including conventional thermal units (CTUs), carbon capture plants (CCPs), energy storage systems (ESSs), and demand response (DR). The operation and regulation characteristics of multiple types of flexible resources are jointly formulated. Moreover, the carbon-capturing mechanism of CCPs is considered to reduce carbon emissions. The operational risk is measured by the conditional value-at-risk (CVaR) to coordinate the wind power accommodation and operational economy. A two-stage robust scheduling model with a flexible uncertainty set is established, which optimizes the energy and reserve scheme in the first stage and checks the feasibility of re-dispatch in the second stage. The column-and-constraint generation (C&CG) algorithm is utilized to solve the proposed robust model. Simulation results on two different scale systems verify the feasibility and effectiveness of the proposed method.

Stochastic uncertainty management in electricity markets with high renewable energy penetration

Abstract The high penetration of renewable energy sources (RESs) in modern power systems poses two conflicting issues. First one is the reduction in the operation costs resulted from RESs utilization instead of the expensive fossil fuel thermal generating units. However, the RESs are characterized by its uncertain and intermittent behavior that their output power is not dispatchable and not known exactly due to forecast errors. Therefore, reserves are scheduled in the day-ahead market to meet the uncertain supply from RESs which add some costs to the system. The decision maker should be aware of those two conflicting objectives in order to operate the system in the optimal way with minimum operation costs. The problem of the market clearing is formulated as Mixed Integer Linear Programming (MILP) problem using GAMS software. It is considered a two stage stochastic programming with the objective of minimizing the expected total operation energy and reserve costs while satisfying the various operational constraints. The results show the effectiveness of the RESs integration in different cases with the consideration of load shedding, RESs curtailment and transmission congestion. As shown from the results, when the network is congested, the operation costs are increased due to the load shedding and RESs curtailment events occur. Moreover, scheduled reserves are increased to face the uncertainty of RESs. As the RESs power penetration increases with no network congestion, a decreasing trend in the operation cost can be seen. However, this trend is less marked as the uncertainty of RESs generation increases. For the network congested case, the reduction rate of the operation cost is decreased for increasing RESs power penetration levels.

Barriers to Transportation in Rural Communities: Perspective of Older Adult Users.

Rural older adults who require transportation assistance face more challenges than their urban counterparts. By focusing on a historically underserved population, this study examined specific barriers from the perspective of older adult users and explored potential policy and technology solutions. A cross-sectional study was conducted throughout rural Wisconsin to identify specific barriers to transportation, uncover personal factors associated with identified barriers, and measure causal relationships between identified barriers and travel satisfaction. A total of 580 older adult respondents from 92% of rural counties across the state provided clear answers regarding specific transportation barriers. Of these, 67.6% identified at least one barrier, but only 12.8% had stopped using transportation due to any identified barriers. Top barriers to accessing transportation included service hours, service areas, trip destinations, getting in/out of service vehicles, service reservations, and operational scheduling. Although specific barriers were associated with different sociodemographics, trip purposes, and frequency of transportation usage, logistic regression findings suggested that concerns about service hours, service areas, and service reservation were the only 3 major determinants driving rural older adults' attitudes toward transportation usage. Rural older adults relying on transportation assistance have demonstrated diverse needs and constraints. Rural transportation could provide better support by extending service availability in terms of hours, areas, and destinations, improving door-to-door accessibility by providing "arm through arm" services, enhancing service responsiveness and reliability via age-friendly technology solutions, and implementing a sliding scale subsidy program that takes income level and trip frequency into account.

A stochastic analysis of the energy and reserve operation for battery storage-assisted prosumer aggregator in the Southwest Power Pool Market

To facilitate the integration of renewable energy in electric power systems, the installed capacity of distributed renewable energy sources has been increased rapidly worldwide in recent years, and the prosumers and prosumer aggregators have become important players in smart grid. To maximize the economic benefits of a prosumer aggregator in power markets, a stochastic model used for generating the day-ahead (DA) energy and operating reserve scheduling strategies is proposed. The uncertainties associated with solar power productions, demands and prices in energy and operating reserve markets are handled via stochastic optimization technique. To manage the potential risks faced by the prosumer aggregator, the conditional value at risk is adopted to measure and manage its potential low profits or losses. Finally, case studies are performed by using the data in the energy and operating reserve markets operated by the Southwest Power Pool (SPP), and the optimal energy and operating reserve bids are generated effectively by solving the proposed model. The values of the operating reserve markets and battery storages are analyzed in detail via simulation, and it shows that higher reserve prices and larger battery storage capacity would lead to higher expected profit and lower risks for the prosumer aggregator.

Examining Ethnicity Conflict among Gujjar-Bakarwal Tribes and Pahari Speaking People in Jammu and Kashmir

The Contemporary Political and Social clash between Gujjar-Bakarwal and Pahari Speaking peoples perhaps one of the most serious challenges to internal peace at micro level and is difficult to handle at large. The Focus of the study is on the recent G.D Sharma Commission report and observations that has proposed granting Scheduled Tribe status to Various Communities namely Pahari, Paddari, Koli and Gadda Brahmin. Gujjar-Bakarwal nomadic community have already scheduled tribe reservation, after the recommendations of G.D Sharma commission, Gujjar Bakarwal started revolting against identifying Paharis as an ethnic group as opposed to a linguistic group because Paharis are already enjoying four percent reservation on the basis of linguistic group (Pahari Speaking People). Gujjar-Bakarwal had already been the victim of social stigma for ages and in such circumstances with the inclusion of Pahari Upper clan in ST would clearly lead to a long term conflict and imbalance between both the communities. Furthermore, if non tribal communities of upper castes for example Jattas, Mirza, Rajputs, Sayeed and Brahmins from Rajouri and Poonch are given STs Status, how will the government prevent a similar demand from others sections like Khan, Gupta and Sharma, a business class living in the plains of Jammu and Chenab Valley? This research paper is to examine the ethnicity conflict based on inclusion of upper caste(Non Tribal) in STs and its impact on real tribal groups; Gujjar, Bakarwal, Gaddi, Sippi, Bot and Dard Sheen. Key Words: Abstract: The Contemporary Political and Social clash between Gujjar-Bakarwal and Pahari Speaking peoples perhaps one of the most serious challenges to internal peace at micro level and is difficult to handle at large. The Focus of the study is on the recent G.D Sharma Commission report and observations that has proposed granting Scheduled Tribe status to Various Communities namely Pahari, Paddari, Koli and Gadda Brahmin. Gujjar-Bakarwal nomadic community have already scheduled tribe reservation, after the recommendations of G.D Sharma commission, Gujjar Bakarwal started revolting against identifying Paharis as an ethnic group as opposed to a linguistic group because Paharis are already enjoying four percent reservation on the basis of linguistic group (Pahari Speaking People). Gujjar-Bakarwal had already been the victim of social stigma for ages and in such circumstances with the inclusion of Pahari Upper clan in ST would clearly lead to a long term conflict and imbalance between both the communities. Furthermore, if non tribal communities of upper castes for example Jattas, Mirza, Rajputs, Sayeed and Brahmins from Rajouri and Poonch are given STs Status, how will the government prevent a similar demand from others sections like Khan, Gupta and Sharma, a business class living in the plains of Jammu and Chenab Valley? This research paper is to examine the ethnicity conflict based on inclusion of upper caste(Non Tribal) in STs and its impact on real tribal groups; Gujjar, Bakarwal, Gaddi, Sippi, Bot and Dard Sheen.

Optimal self-scheduling for a multi-energy virtual power plant providing energy and reserve services under a holistic market framework

This paper addresses a self-scheduling model for a multi-energy virtual power plant (MEVPP) to optimize its day-ahead energy and reserve schedules considering the participation in joint markets. The coordination of energy and reserve services is realized by developing a holistic market framework. MEVPP trades electric energy in day-ahead market and natural gas in natural gas market under the uniform price scheme. MEVPP provides reserve in ancillary service market under the pay-as-bid scheme considering uncertain market clearing prices. Reserve regulations are modeled for the reserve quality provided by MEVPP. MEVPP can sign contracts in capacity market for capacity adequacy. The electricity and natural gas imbalance payments and unsupplied reserve penalty resulting from uncertain PV generation are calculated in real-time. The case studies, based on the practical data from Italian power exchange and transmission system operator, show the economic achievements of MEVPP with multiple markets participation. The advantages of multi-energy coupling in improving flexibility and economic profit are numerically analyzed. MEVPP is proven to be a promising reserve service supplier for TSO. Because through reasonable regulations, the reserve quality of MEVPP can be improved with little impact on its total cost.