Demand Uncertainty Research Articles

Optimal coordinated energy management in active distribution networks considering battery energy storage and price‐responsive demand

AbstractContemporary distribution networks can be seen with diverse dispatchable and non‐dispatchable energy resources. The coordinated scheduling of these dispatchable resources with non‐dispatchable resources can provide several techno‐economic and social benefits. Since battery energy storage systems (BESSs) and microturbine units are capital intensive. A thorough investigation of their coordinated scheduling on a purely economic basis will be an interesting and challenging task while considering dynamic electricity price and uncertainty of renewable power generation and load demand. This paper proposes a new methodology for optimal coordinated scheduling of BESSs and microturbine units considering existing renewable energy resources and dynamic electricity price to maximize daily profit function of the utility. In this study, a recently explored modified African buffalo optimization algorithm is employed. The key attributes of the proposed methodology are comprised of mean price‐based adaptive scheduling embedded within a decision mechanism system to maximize arbitrage benefits. Decision mechanism system keeps a track of system states as a‐priori thus guides the artificial intelligence‐based solution technique for sequential optimization. This may also reduce the computational burden of complex real‐life engineering optimization problems. Further, a novel concept of fictitious charges in coordination with BESS management algorithm is proposed to restrict the counterproductive operational management of BESSs. The application results investigated and compared on a benchmark 33‐bus test distribution system highlights the importance of the proposed methodology.

Pre-Disaster Optimal Operation Strategy for Hydrogen-Fuel-Based Isolated Power System with Disaster Uncertainties

The increasing occurrence of severe weather phenomena presents substantial difficulties for electricity distribution systems. This study investigates the integration of hydrogen production plants (HPPs) into power distribution networks to bolster resilience against the increasing frequency of extreme weather events. It highlights the innovative use of hydrogen, generated from renewable sources, as an energy storage medium to ensure a stable power supply during disruptions. By employing stochastic optimization, the study aims to effectively manage hydrogen production and utilization, considering uncertainties in disaster scenarios and energy demands. It addresses critical research gaps, such as the lack of focus on pre-disaster preventive scheduling using hydrogen, the under-explored application of stochastic optimization in such contexts, and the predominance of real-time response strategies over pre-emptive measures. This approach significantly advances current understanding by proposing novel strategies that leverage hydrogen production and sophisticated optimization to enhance the resilience of power networks against extreme weather events. In some scenarios, using our method can reduce the cost of pre-disaster prevention by approximately 15.68% while ensuring that the disaster recovery effect remains unchanged.

GIS-Based Optimization for Gas Distribution Route Design: A Case Study of PT Gagas Energi Indonesia

PT Gagas Energi Indonesia is a company engaged in the trading and natural gas business, one of which is Gaslink C-Cyl. Route determination for Gaslink C-Cyl distribution is carried out subjectively based on the proximity of locations between customers, thus creating inefficient routes, affecting the number of vehicles needed, and causing high transportation costs. Based on this problem, this study aims to minimize transportation costs by determining the optimal route and number of vehicles needed using the Multi-Trip Vehicle Routing Problem Time Windows Simultaneous Pickup and Delivery (MTVRPTW-SPD). This problem is solved using Geographic Information System (GIS) based optimization. To evaluate GIS-based optimization performance, comparison between existing condition and optimization are done for 50 demand points. Then, two scenario was developed, 100 and 200 demand points to explain the decision implication related to demand uncertainty. Based on the optimization process for 50 demand points gives better solution than existing condition in term of number of fleets needed, total distance travelled, and total transportation cost which result 3 units, 46,5%, and 43,5% respectively.

A Two-Stage Robust Pricing Strategy for Electric Vehicle Aggregators Considering Dual Uncertainty in Electricity Demand and Real-Time Electricity Prices

To enable the regulation and utilization of electric vehicle (EV) load resources by the power grid in the electricity market environment, a third-party electric vehicle aggregator (EVA) must be introduced. The strategy of EVA participation in the electricity market must be studied. During operation, the EVA faces a double uncertainty in the market, namely, electricity demand and electricity price, and must optimize its market behavior to protect its own interests. To achieve this goal, we propose a robust pricing strategy for the EVA that takes into account the coordination of two-stage market behavior to enhance operational efficiency and risk resistance. A two-stage robust pricing strategy that takes into account uncertainty was established by first considering day-ahead pricing, day-ahead electricity purchases, real-time electricity management, and EV customer demand response for the EVA, and further considering the uncertainty in electricity demand and electricity prices. The two-stage robust pricing model was transformed into a two-stage mixed integer programming by linearization method and solved iteratively by the columns and constraints generation (CCG) algorithm. Simulation verification was carried out, and the results show that the proposed strategy fully considers the influence of price uncertainty factors, effectively avoids market risks, and improves the adaptability and economy of the EVA’s business strategy.

RETRACTED: Cost-based distributionally robust tourist attraction recommendation with demand uncertainty

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.

Monte Carlo analysis of masonry structures under tsunami action: Reliability of lognormal fragility curves and overall uncertainty prediction

Tsunami vulnerability of coastal buildings has gained more and more interest in recent years, in the consciousness of what losses may be caused. The improvement of the available approaches for the quantitative estimation of the probability of building damage and for defining possible strategies for risk mitigation is an actual goal. In this framework, several authors have provided empirical fragility curves based on field surveys after tsunamis. Nevertheless, a predictive approach based on analytical fragility curves, which can be extended to many classes of buildings, is essential for the scopes of civil protection and risk mitigation. In this paper, an approach for the construction of fragility curves, proposed for masonry structures under tsunami waves, is discussed and refined in the part regarding the assignment of the uncertainties. Further, an assessment of the reliability of the lognormal fragility distribution is carried out based on a Monte Carlo simulation applied to 4 classes of buildings. Here, it is shown that Monte Carlo analysis allows a direct evaluation of the uncertainties without the need to resort to ambiguous regression analyses and rules of combination of the uncertainties of demand and capacity based on the regression analysis results or other uncertainty estimation approaches.

Two-stage stochastic optimization of virtual power plant with wind power and uncertain demand

ABSTRACT The virtual power plant (VPP) has been recognized as an effective way to facilitate penetration of renewable and distributed energy resources in electricity markets. This paper introduces an adaptive curtailment strategy for a VPP comprising a wind power plant and uncertain demand, and explores the economic advantage of adaptively adjusting wind power curtailment. A two-stage stochastic model is proposed to deal with the uncertainties in wind power generation (WPG), load demand and market prices. In the model, the bid decision is made in the face of uncertainties in the first stage, while the control (curtailment) decision is made based on realized uncertain parameters in the second stage. This paper provides the closed-form optimal curtailment decision and characterizes the optimal bid decision. An efficient binary search algorithm is developed for optimizing the bid decision. By using a distribution-free approach, we show that as the prediction accuracy of WPG improves, the optimal bid decision converges toward the expected minimal power exchange, leading to a decrease in expected operational cost with diminishing marginal return. Numerical experiments based on real-world data demonstrate that compared with the existing greedy strategy and coordinated strategy, the proposed model can decrease the expected operational cost up to 16.9% and 11.0%, respectively.

Design of small-scale hybrid energy systems taking into account generation and demand uncertainties

The adoption of energy systems powered by renewable sources requires substantial economic investments. Hence, selecting system components of an appropriate size becomes a critical step, which is significantly influenced by their distinct characteristics. Furthermore, the availability of renewable energy varies over time, and estimating this availability introduces considerable uncertainty. In this paper, we present a technique for the optimal design of hybrid energy systems that accounts for the uncertainty associated with resource estimation. Our method is based on stochastic programming theory and employs a surrogate model to estimate battery lifespan using a feedforward neural network (FFNN). The optimization analysis for system design was conducted using a genetic algorithm (GA) and the poplar optimization algorithm (POA). We assessed the effectiveness of the proposed technique through a hypothetical case study. The introduction of a surrogate model, based on an FFNN, resulted in an approximation error of 9.6 % for cost estimation and 20.6 % for battery lifespan estimation. The probabilistic design indicates an energy system cost that is 25.7 % higher than that obtained using a deterministic approach. Both the GA and POA achieved solutions that likely represent the global optimum.

Backordering policy for hi-tech products with price and demand uncertainty during the pandemics

Backordering policy for hi-tech products with price and demand uncertainty during the pandemics

Energy management for scalable battery swapping stations: A deep reinforcement learning and mathematical optimization cascade approach

The rapid growth of electric vehicles is driving the expansion of scalable Battery Swapping Stations (BSSs) to meet the demand for fast charging. However, existing BSS energy management struggles to adapt to the changes in battery counts online, the uncertainty of electricity prices and battery demand, as well as the complexity of demand response (DR). To address these issues, we propose a cascading approach that combines Deep Reinforcement Learning (DRL) with Mathematical Optimization (MO). Firstly, our method employs a two-layer optimization framework and establishes a BSS model. The upper controller uses DRL to make sequential decisions on total power for each time slot, addressing sequential decision-making under uncertainty. The lower controller uses MO to allocate total power to each charging bay for a single time slot, addressing space complexity. Secondly, we tailor proximal policy optimization algorithms and mixed-integer quadratic programming algorithms for BSS DR. The DRL is universally adaptable to scalable BSS, while the MO automatically generates and solves optimization models based on the working charging bay count. The proposed method is at least 33 times faster than existing methods and performs well in scalable BSSs under uncertain conditions. It can converge within a typical BSS DR scheduling slot of 15 min based on an i7-8950H CPU, even for a BSS with 3200 charging bays.

Multistage scenario-based planning for locating of pharmaceutic distribution centers for emergency patients by considering practical constraints under budget uncertainty conditions (case study: Kerman city in Iran)

In the realm of healthcare, medicine and the pharmaceutical supply chain exert notable influence. Recognized as a strategic commodity, any disruption in its supply chain can precipitate significant crises. The dispersed nature of pharmacies and the critical need for prompt medication delivery to patients underscore the paramount importance of designing an optimal pharmaceutical supply chain. This necessity is particularly pronounced when addressing the needs of diabetic and epilepsy patients, who, being more susceptible and in urgent need of medication, intensify the significance of a robust supply chain. Given the critical role of medicine and the pharmaceutical supply chain in healthcare, the repercussions of supply chain disruptions are far-reaching. A well-designed pharmaceutic supply chain is indispensable, especially in ensuring timely access to medications for vulnerable patient groups. To address this imperative, this study proposes a multi-stage scenario-based mathematical model aimed at enhancing the responsiveness of demand regions amidst uncertain budgets and feasible scenarios for meeting the insulin and phenobarbital requirements of patients. By incorporating uncertainties in both demand and procurement costs, the practicability of applying this model in real-world contexts has been strengthened. A case study focusing on Kerman City in Iran is conducted to evaluate the efficacy of the proposed model.

Joint planning of residential electric vehicle charging station integrated with photovoltaic and energy storage considering demand response and uncertainties

Residential electric vehicle charging station integrated with photovoltaic and energy storage represents a burgeoning paradigm for the advancement of future charging infrastructures. This paper investigates its planning problem considering multiple load demand response and their uncertainties. First, a hybrid time series and Kalman Filter model is proposed for photovoltaic output prediction. Second, an orderly charging model and an incentive scheduling model are developed for electric vehicles to facilitate both price-based and incentive-based demand responses. Third, to address uncertainties in user response behavior, consumer psychology theory is applied to construct fuzzy response models for both charging and residential loads. Finally, a multi-objective capacity allocation model is constructed and optimized from the perspectives of economy, environment and safety. The simulation case studies the impact of different demand response strategies and their uncertainties on the planning results. The findings indicate that implementing multiple demand response strategies significantly increases annual revenue by 295.82 %, while reducing carbon emissions and power fluctuations by 16.48 % and 44.27 %, respectively.

A mean field game model for optimal trading in the intraday electricity market

AbstractIn this study, we provide a simple one period mean-field-games setting for the joint optimal trading problem for electricity producers in the electricity markets. Based on the Markowitz mean-variance approach from stock trading, we consider a decision problem of an electricity provider when determining the optimal fractions of production that should be traded in the day-ahead and in the intraday markets. Moreover, all such providers are related by a ranking criterion and each one wants to perform as good as possible in this ranking. We first start with a simple model where only the price risk in the intraday market is present and subsequently extend the problem to the cases involving either production and/or demand uncertainty. The key technique is to reduce the optimality conditions to a first order non-linear ordinary differential equation. We will illustrate our findings by various numerical examples. Our findings will in particular be important for electricity producers using renewable resources.

Capacity Demand Analysis of Rural Biogas Power Generation System with Independent Operation Considering Source-Load Uncertainty

With the greatly increased penetration rate of wind power, photovoltaic, and other new energy sources in the power system, the proportion of controllable units gradually decreased, resulting in increased system uncertainty. The biogas power generation system can effectively alleviate the pressure caused by source-load uncertainty in such high-permeability systems of new energy sources such as wind power and photovoltaic. Hence, from the perspective of the power system, this paper introduces a capacity demand analysis method for a rural biogas power generation system capable of independent operation amidst source-load uncertainty. To enhance the depiction of pure load demand uncertainty, a scene set generation method is proposed, leveraging quantile regression analysis and Gaussian mixture model clustering. Each scene’s data and probability of occurrence elucidate the uncertainty of pure load demand. An integrated optimal operation model for new energy and biogas-generating units, free from energy storage capacity constraints, is established based on the generated scenario set. Addressing considerations such as biogas utilization rate and system operation cost, a biogas storage correction model, utilizing the gas storage deviation degree index and the cost growth rate index, is developed to determine biogas demand and capacity. The example results demonstrate the significant reduction in gas storage construction costs and charging and discharging imbalances achieved by the proposed model while ensuring systemic operational cost effectiveness.

Identification of critical links based on the optimal reliable path in stochastic traffic networks.

In urban stochastic transportation networks, there are specific links that hold great importance. Disruptions or failures in these critical links can lead to reduced connectivity within the road network. Under this circumstance, this manuscript proposed a novel identification of critical links mathematical optimization model based on the optimal reliable path with consideration of link correlations under demand uncertainty. The method presented in this paper offers a solution to bypass the necessity of conducting a full scan of the entire road network. Due to the non-additive and non-linear properties of the proposed model, a modified heuristic algorithm based on K-shortest algorithm and inequality technical is presented. The numerical experiments are conducted to show that improve a certain road link may not necessarily improve the overall traffic conditions. Moreover, the results indicate that if the travel time reliability is not considered, it will bring errors to the identification of key links.

Supply chain structure, debt financing and product market competition

AbstractThis paper studies the interactions of capital structure and product market competition under two supply chain structures: a distribution structure where two competing retailers source from a common supplier and a parallel structure where each retailer sources from a dedicated supplier. The analyses demonstrate that if the retailers have access to external credits, they will borrow more (less), buy more (less), and incur a higher (lower) default probability under a parallel structure than under a distribution structure if the retailers sell substitutes (complements). The retailers' leverage improves the profit of their upstream suppliers and of the channel but improves their own profits only if they sell complements. If they sell substitutes, borrowing makes them worse off, leading to the prisoner's dilemma phenomenon. If only one retailer has access to external credit, then it will enjoy a leadership premium (loss) if the retailers sell substitutes (complements), and this effect trickles up to the upstream supplier of the leveraged retailer under the parallel structure. Thus, the dedicated suppliers will abet the leveraged retailers to compete more aggressively than unleveraged ones. However, the common supplier is independent of their downstream customer's capital structure. These results reveal that the impacts of external credit on the supply chain profitability depend on the stages the supply chain members are in, the degree of product differentiation, the supply chain structure, the uncertainty of demand, and whether the competitor also has access to external credits.

Capacity Allocation in Cancer Centers Considering Demand Uncertainty

This paper introduces a model to aid decision-makers in answering many of the important questions regarding how best to operate a cancer center. This study aims to allocate the available cancer center capacity to different cancer types to minimize the deviation in patient demand satisfied from desired supply targets across multiple cancer types. A stochastic chance-constrained model is proposed to consider uncertainties in new and returning patient demand. The proposed model determines the optimal specialization mix for oncologists based on the distribution of demand by cancer type, preventing potential mismatches. Additionally, it aims to balance workloads among oncologists and individual clinics and indirectly reduce support service costs by limiting their clinic days. Numerical results are presented using historical data collected from our collaborating cancer center to demonstrate the usefulness of the model. The results confirm that the ability to satisfy patient demand increases as oncologists become more flexible. In addition, the results show that even having a small number of highly flexible oncologists is sufficient to achieve strong patient demand satisfaction. Moreover, restricting the allowable workload difference among oncologists achieves an acceptable trade-off between workload balance and satisfying patient demand.

Allocate electric vehicles’ public charging stations with charging demand uncertainty

Nowadays, electric vehicles (EVs) have developed rapidly due to their great advantages in fuel savings, reduced greenhouse gas emissions, and low air pollution. However, charging imbalances and shortages have limited the prevalence of EVs. We optimize the siting and sizing of public charging stations under uncertain urban en-route recharging demand. Considering charging congestion, we introduce the Erlang’s loss formula to represent the service level requirement of each charging station. First, we determine the optimal locations and sizes of those stations by a robust optimization model, where we figure out its deterministic dual following linear approximation of the loss rate in queueing models. We further use a discrete-event simulation to relax the assumption of time-independent charging demand in the optimization approach and model real-time traffic based on real-world traffic and power grid information in Nanjing, China. Thus, we verify the robust optimum outperforming deterministic and stochastic optimums.

Improving aggregate production planning considering maximum inventory area and service level with demand uncertainty: a nearshoring context in Mexican companies

ABSTRACT Nearshoring is a phenomenon that has gained prominence in Mexico over the last 2 years, where companies have faced problems in meeting customer demand due to a service level and limited inventory space. Previous studies in aggregate planning did not consider the possibility of allowing rental of extra warehouse space if needed with a concurrent uncertainty in demand. Therefore, to address this gap in the research, an improved optimization model for multi-product and multi-period aggregate planning with uncertainty in demands given an area of inventory space is proposed. A set of cases based on real industry data were solved and compared to a model where no warehouse space is allowed to be rented. The results are improvements related to production costs and customer demand satisfaction. In addition, the model will help managers of manufacturing companies to determine the minimum inventory space required to effectively meet demand.

Electric-vehicle charging facility deployment models for dense-city residential carparks considering demand uncertainty and grid dynamics

As the driving range of electric vehicles (EVs) increases, home-based charging has been becoming the dominant strategy for EV users to replenish electricity in urban e-mobility systems. In Asian metropolises with high population density (called dense-cities), the majority of residents live in multi-unit dwellings and share public parking and charging facilities in their residential carparks. This study, therefore, investigates the EV charging facility deployment (CFD) problem for dense-city residential carparks, taking into account charging demand uncertainty and the impact of grid dynamics. To address this problem, we first develop a water-pipe model to minimize the construction cost of the CFD scheme while balancing dynamic charging demand and supply. We then extend our approach to formulate a chance-constraint optimization model that considers more practical factors such as stochastic and dynamic charging demand, multi-type chargers, grid dynamics and the setup time of charging service. Furthermore, we propose a simulation-based method to validate the developed CFD models at the operational level. Our case study in Singapore demonstrates that the chance-constraint optimization model produces effective CFD solutions for all simulated charging scenarios. Our results also reveal the importance of considering grid dynamics and charging demand uncertainty for the residential-carpark CFD problem.