Operational Planning Model Research Articles

Dynamic Operational Planning in Warfare: A Stochastic Game Approach to Military Campaigns

ABSTRACTWe study a two‐player discounted zero‐sum stochastic game model for dynamic operational planning in military campaigns. At each stage, the players manage multiple commanders who order military actions on objectives that have an open line of control. When a battle over the control of an objective occurs, its stochastic outcome depends on the actions and the enabling support provided by the control of other objectives. Each player aims to maximize the cumulative number of objectives they control, weighted by their criticality. To solve this large‐scale stochastic game, we derive properties of its Markov perfect equilibria by leveraging the logistics and military operational command and control structure. We show the consequential isotonicity of the optimal value function with respect to the partially ordered state space, which in turn leads to a significant reduction of the state and action spaces. We also accelerate Shapley's value iteration algorithm by eliminating dominated actions and investigating pure equilibria of the matrix game solved at each iteration. We demonstrate the computational value of our equilibrium results on a case study that reflects representative operational‐level military campaigns with geopolitical implications. Our analysis reveals a complex interplay between the game's parameters and dynamics in equilibrium, resulting in new military insights for campaign analysts.

A hierarchical framework for integration of smart buildings in fully-renewable multi-microgrids and distribution systems: Towards more sustainable societies

Using smart buildings would be a crucial step towards sustainability. Although in the literature, some research effort has focused on integration of smart buildings into power systems; the integration of fully-renewable smart buildings into fully-renewable microgrids and distribution systems has not been addressed before. In this paper, a tri-level hierarchical stochastic framework is developed for the integration of fully renewable smart buildings in fully renewable multi-microgrids (MGs) and distribution systems. The studied smart buildings have electric water heater, air conditioner, PV and battery. In the first level, the model of each smart building is separately solved to find optimal schedule of its resources and the desired electricity exchange with its corresponding MG. In the second level, each MG solves its operational planning model, considering its transactions with the connected smart buildings and finally, in the third level, distribution system operator solves its operational planning model, accepting the electricity exchange requests taken from all MGs. The proposed methodology is applied to a 69-bus 100 % renewable distribution system with 4 100 % renewable microgrids, integrated with 100 % renewable smart buildings. The results confirm the efficacy of the proposed methodology for the integration of 100 % renewable smart buildings in 100 % renewable multi-MGs. The results testify that that building-to-grid (B2G) capability decreases the operation cost of all smart buildings; the average decrease in operation cost of buildings, caused by B2G, is 38 %. The results on the studied case indicate that that the impact of batteries on operation cost of smart buildings is marginal. The results also show that the higher threshold of comfort index in smart buildings, results in their higher operation cost. The results of this research may be helpful for both smart building operators and power system operators. This research is in line with the global energy transition and decarbonisation targets as the considered smart buildings, microgrids and distribution system are fully renewable.

The Optimal Selection of Renewable Energy Systems Based on MILP for Two Zones in Mexico

This paper presents a series of enhancements to a previously proposed mixed-integer linear programming (MILP) model for investment decisions and operational planning in distributed generation (DG) systems. The main contribution of this study consists of integrating a wind generation system and multiple loads at different buses in a network. The model considers dynamic weather data, energy prices, costs related to photovoltaic and wind systems, storage systems, operational and maintenance costs, and other pertinent factors, such as efficiencies, geographical locations, resource availability, and different load profiles. The simulation results obtained through implementation in Julia’s programming language illustrate that the MILP formulation maximizes the net present value, and four configurations for hybrid power generation systems in Mexico are analyzed. The objective is to enable profitability assessment for investments in large-capacity DG systems in two strategic zones of Mexico. The results show that the configurations in the NE zone, especially in Tamaulipas, are the most cost-effective. Case 1 stands out for its highest net present value and shortest payback time, while Case 2 offers the highest energy savings. In addition, Cases 3 and 4, which incorporate storage systems, exhibit the longest payback periods and the lowest savings, indicating less favorable economic performance compared with Cases 1 and 2. Moreover, the sales of two case studies, one without a storage system and the other with a storage system, are shown. The model also incorporates instruments for buying or selling energy in the wholesale electricity market, including variables that depict the injected energy into the electrical grid. This comprehensive approach provides a detailed overview of optimal energy management.

Synthetic wind speed time series generation by dynamic factor model

The global transition to renewable energy is driven by the fight against climate change. Wind power plays a crucial role in reducing dependence on fossil fuels and greenhouse gas emissions. Therefore, addressing uncertainties in wind speed variations requires innovative solutions. This study proposes a Bayesian-based approach using a Dynamic Factor Model to generate synthetic monthly average wind speed series. The Dynamic Factor Model framework captures temporal and spatial correlations, improving wind resource representation in operational planning models. The model's autoregressive configuration with common factors, prior distributions, and Bayesian inference techniques enhances predictive capabilities. Validation exercises confirm the model's reliability, accurately capturing seasonal oscillations and spatial correlations across eight wind farms. The study highlights the usefulness of the Dynamic Factor Model in evaluating wind projects and optimizing energy generation strategies, effectively mitigating wind uncertainty, and facilitating renewable energy integration in Brazil's power mix.

Dynamic Operation Planning Model of Air-Conditioning Based on Weather Forecast: Heat Resource Usage Considering Energy Saving and Room Comfort

Air-conditioning systems must save energy while maintaining an appropriate room environment. In heat management, there is a tradeoff between the requirement to save energy during heat production and individual heat consumption for maintaining comfort. Heat consumption is dependent on environmental conditions in a given room, including the thermal load of the room and ambient weather. To predict thermal load, regression models can be built using weather forecast data, and the heat level necessary to avoid heat stroke while maintaining comfort can be calculated. In this study, we propose a decision-making process model of heat resource allocation for facility managers, known as a dynamic air-conditioning operation plan. The heat level allocated may change, considering the gap between planned and actual heat usage on a daily and monthly basis, and the resource is then distributed according to the thermal load. If the actual heat usage is expected to exceed the monthly target by the middle of the month, the plan is then revised to use the heat resource allocated for the following month, ahead of schedule. To demonstrate effectiveness of the model, sensitivity analysis was performed based on measured data.

A hierarchical model for strategic and operational planning in blood transportation with drones.

Blood transportation is a critical aspect of the healthcare systems, ensuring whole blood and blood products are delivered to patients in a timely and efficient manner. However, transportation of blood and other medical supplies can be challenging, especially in urban areas with limited infrastructure and heavy traffic. Drones have become increasingly important in recent years as a means of delivering medical supplies, including blood, due to their ability to provide fast, reliable, and cost-effective transportation. This study proposes two mathematical programming models in the hierarchical structure to improve decision-making for strategic and operational planning in the blood supply chain network. The limited information available in strategic planning presents risks to the blood supply chain, making it imperative to address uncertainties. To tackle this challenge, a novel approach called Scenario-based Robust Bi-objective Optimization has been proposed. The first model employs this approach to efficiently handle demand uncertainty by simultaneously maximizing the covered demand and minimizing costs. The model is subsequently solved using the augmented ε-constraint method. The second model is a routing-scheduling operational model that aims to minimize the sum of operations time, taking into account time windows for blood collection centers and hospitals. The developed hierarchical model is implemented in a three-level supply chain of Tehran province under three crisis scenarios in different parts. The findings and analysis of this implementation suggest that it is beneficial to set up drone stations in cost-effective and central locations to avoid costly network design. Furthermore, utilizing the minimum number of feasible drones enhances operational time and results in cost savings and increased efficiency. Overall, this study highlights the potential of using drones for blood transportation in urban settings, which can have significant implications for improving the quality of healthcare delivery.

Coordinated active and reactive power management for enhancing PV hosting capacity in distribution networks

AbstractThis paper proposes an operational planning model based on optimal active and reactive power control strategies to enhance solar photovoltaics (PV)’ hosting capacity in distribution networks. Reactive power control is carried out through optimum static VAr compensators (SVCs) placement, while active power control is performed through flexible loads, particularly shiftable and interruptible loads. The first stage of the proposed two‐stage stochastic model assigns decision‐making regarding calculating PV hosting capacity at different nodes, in addition to the allocation and capacity of SVCs. In the second stage, the first stage decisions are assessed to ensure the power flow constraints under various uncertainties such as daily load and stochastic PV generation. The presented model is investigated through numerical analyses on modified IEEE 15‐bus and IEEE 33‐bus distribution systems considering different active‐reactive strategy cases. While most previous works only rely on one type of active or reactive power control strategy, this study investigates the challenges of the respective application of active and reactive power control in various modes of fundamental practices. The obtained results prove the superiority of the proposed hybrid active‐reactive control strategy for enhancing PV hosting capacity compared to respective active or reactive power controls.

Simulation Model for Operational Planning of City Cargo Transportation by Trams in Conditions of Stochastic Demand

A city’s transport and distribution system requires the effective organization of urban freight deliveries that take into consideration the economic, ecological, and social impact. Implementation of the concept of green logistics necessitates the use of environmentally friendly and energy-efficient modes of transport, which consider the existing infrastructure and the possibility of its development. The aim of this work was to facilitate the transfer to rail transport, using the existing tramway infrastructure, those shipments which previously entered the city center by road. The paper proposes a simulation model for small consignments delivered by freight trams in the city of Poznań, Poland. Operational planning is carried out in conditions of uncertainty and risk. The uncertainty is due to the stochastic nature of the demand for products. The risk is characterized by the probability that, due to technological and technical limitations, a given customer will not be fully served from the distribution center. The authors provide a decision support tool for evaluating the possible locations of tram depots and for route planning. The assignment of the routes, with the criterion of minimizing the costs for the carrier, and with a limitation on tram operating time, is based on the use of genetic algorithms, which makes it possible to obtain a solution that is close to optimal, within a reasonable time period. As a result of a series of simulation experiments and statistical data processing, the distribution laws and expected average values of the technological parameters were determined for the functioning of the city transport and distribution system under conditions of uncertainty and risk.

A Network Flow Model for Operational Planning in an Underground Gold Mine

In underground mines, the problem of efficiently scheduling and allocating weekly operations has a major impact on the long-term productivity of the mine. The problem of selecting the optimal locations for operations in an underground gold mine is a complex task. It is not solved by simply selecting the levels with the richest grade because the transportation network for ore in an underground mine has a diverse set of capacity constraints that can frustrate immediate mining of all the richest levels. To solve this scheduling difficulty, we formulated a new mixed-integer network flow model of the problem of weekly allocating mining operations in an underground goldmine such that the total gold mined (in ounces) was maximized subject transportation capacity constraints. The model was applied an underground gold mine in Red Lake, Ontario, Canada. The results were compared to those of a two greedy heuristic models that were designed to represent the decision-making heuristics that are currently used at the mine. It was found that the new model yielded solutions that improved upon the two greedy heuristics by 14.7% and 6.0%, respectively. The results of this research illustrate that the development of this optimization model can support decisions to improve a gold mine’s productivity.

Cooperative Operational Planning Model for Distributed Energy Resources With Soft Open Point in Active Distribution Network

In this paper, optimal cooperative operational planning of distributed energy resources (DER) in an active distribution network (ADN) with soft open points (SOP) has been investigated. To do so, a new two-layer coordinated optimization framework ADN has been proposed considering SOP. In the upper layer, the location and sizing of the DERs and SOPs has been optimised. While inner layer executes simultaneous optimal functioning of volt/var control (VVC) devices, remote controlled switches (RCS), DERs, and SOPs. The main objective of proposed methodology is to reduce total investment cost of DERs and SOPs as well as operating costs of the system over the planning horizon. Besides, objective function considers the cost of energy not served (ENS), the cost of substation-purchased electricity, and the cost of carbon emissions. Meanwhile, a stochastic module has been employed to address high-level uncertainties associated to renewables and load demands. Besides, the effects of distribution network reconfiguration (DNR) and conservation voltage reduction (CVR) have been taken into account. For various instances, the suggested framework was implemented on an IEEE 119-bus distribution system and solved using a proposed hybrid optimization solver. The efficacy of proposed DER with SOP methodology has been validated on various cases and also tested on sudden external disturbances such as under voltage and over voltage problem conditions. When compared to standard planning schemes, the test results show that it is beneficial in boosting system efficiency, increasing reliability, and lowering the carbon footprint of distribution systems.

A Network Flow Model for Operational Planning in an Underground Gold Mine

The difficulty of effectively planning and assigning weekly activities has a significant influence on the long-term productivity of an underground mine. It is an especially difficult task to choose the best places for operations inside an underground gold mine. It cannot be resolved by only selecting the levels with the highest grade of ore because the underground mine’s ore transport network has a range of capacity limitations that may prohibit the immediate mining of all the levels with the highest grade. To solve this scheduling difficulty, we formulated a new mixed-integer network flow model of the problem of weekly allocating mining operations in an underground gold mine such that the total gold mined (in ounces) was maximized subject to the transportation capacity constraints. The model was applied to an underground gold mine in Red Lake, Ontario, Canada. The results were compared to those of two greedy heuristic models that were designed to represent the decision-making heuristics that are currently used at the mine. It was found that the new model yielded solutions that improved upon the two greedy heuristics by 14.7% and 6.0%, respectively. The results of this research illustrate that the development of this optimization model can support decisions to improve a gold mine’s productivity.

Storage and demand response contribution to firm capacity: Analysis of the Spanish electricity system

Provision of firm capacity will become a challenge in power systems dominated by renewable generation. This paper analyzes the competitiveness and role of battery storage, six types of pumped-hydro storage, open cycle gas turbine (OCGT), and demand response (DR) technologies in providing the firm capacity required to guarantee the security of supply in a real-size power system such as the Spanish one in horizon 2030. The paper contributes with detailed and realistic modeling of the DR capabilities. Demand is disaggregated by sector and activities and projected towards 2030, applying a growth rate by activity. The load flexibility constraints are considered to ensure the validity of the results. A generation operation planning and expansion model, SPLODER, is conveniently upgraded to properly represent the different storage alternatives addressed in the paper. The results highlight the importance of considering demand response for evaluating long-term firm capacity requirements, showing a non-negligible impact on the investment decisions on the amount of firm capacity required in the system and the optimal shares of wind and solar PV renewable generation. Results also show the dominance of cost-competitiveness of pumped hydro and OCGTs over batteries. Additionally, capacity payments are required to support firm capacity providers’ investments.

Operational Planning and Design of Market-Based Virtual Power Plant with High Penetration of Renewable Energy Sources

Renewable energy sources (RESs) are becoming more prevalent as a source of clean energy, and their integration into the power market is speeding up. The fundamental reason for this is the growing global concern about climate change. However, their weather-dependent and uncertain nature raise questions about grid reliability particularly, when photovoltaics (PVs) and wind turbines (WTs) technologies are used. As a result, rationally managing Energy Storage Systems (ESSs) under the virtual power plant (VPP) setting is being encouraged as a way of minimizing the impact of the uncertain nature of renewable energies. A VPP is comparatively a new concept that aggregates the capacities of dispatchable and non-dispatchable energy sources, electrical loads, and energy storage systems for the purpose of improving energy supply and demand imbalance. It enables individual consumers and producers to participate in the power markets. In this study, a new market-based (MB)-VPP operational planning model is designed and developed with the aim to evaluate the optimal active power dispatched by (WT, PV, and ESS) operating in the day-ahead power market to maximize the social welfare (SW) of the market. SW can be described as the maximization of the consumer’s benefit function minus the cost of energy generation. The optimization process was carried out by using a scenario-based approach to model the uncertainties of renewable energy sources (i.e, WTs & PVs) and load demand. The proposed model and method performance is validated by simulation studies on a 16-bus UK generic distribution system (UKGDS). The simulation results reveal that the proposed approach maximizes overall system social welfare. The capacity of total active power dispatched by (WT, PV, and ESS) has a positive impact on the VPP profit maximization. This empirical study could be used as a reference baseline model for other energy services providers interested in conducting similar research in the future.

A long-term capacity investment and operational energy planning model with power-to-X and flexibility technologies

In this research, we present a new long-term energy planning model that considers endogenous capacity investment, energy dispatch, Power-to-X, and demand response technologies. A thorough literature review of existing energy planning models is also presented, allowing to present the distinctive characteristics of the proposed model. The proposed model considers an energy system with the objective of minimizing the total capacity investment cost, throughout all technologies, and the operational cost faced by the system in satisfying energy demand. The model also considers the links among different demand sectors, including the links between the electricity, industry, heat, transport, and electro-fuels (e.g., Hydrogen) sectors. The proposed model is used to study the decarbonization of the Croatian energy system under distinct policies associated to RES levels and CO2 emissions goals. We demonstrate that Power-to-X technologies can certainly provide the flexibility that is required by new capacity investments in variable renewable energy sources, obtaining systems with lesser levels of critical excess of energy production. Higher usage of battery storage and Power-to-heat technologies are adopted primarily for variable renewable shares and CO2 reductions of close to 80%, while below such levels, the adoption of such technologies is limited. Additionally, Power-to-heat flexibility options become the major technologies when limits on CO2 emissions from the heating sector are imposed and, particularly, when the variable renewable energy shares in the electricity sector gets close to levels of 60%.

An Operation Planning Model in a Reconfigurable Manufacturing System with Sustainability

Knowledge of the principles of doing proper exercise during pregnancy have always emphasized by professionals, would bring many benefits to both mother and fetus. This cross-sectional study is done to assess the awareness towards the sport activities during pregnancy and some of its associated factors with the field method through using cluster sampling was conducted on 110 pregnant women who were 32 to 40 weeks, healthy and low risk that referred to health centers in Golestan in 2013 (study population) who had no known medical and midwifery disease. Data were collected using a researcher-made questionnaire with demographic and internal reliability (α = 0.7) by interview. At the end the data were analyzed with descriptive statistics (mean, frequency) and illative (two independent samples T-test, analysis of variance of a variable, Pearson and Spearman correlation coefficient).The results showed that the average scores of awareness was13.98 ± 99.50. 1.8% of subjects had a very poor awareness, 9% of subjects had poor knowledge, 50.4% of subjects had moderate knowledge, 27% of subjects had good knowledge and 11.8% of subjects were very well aware. There was a significant difference between the age, level of education of subjects and their husbands’ education with sports knowledge. But there was no significant difference between the ethnicity, occupation, and husband`s job with knowledge. The results showed that the majority of pregnant women do not have good knowledge about doing exercise during pregnancy.

Enhancing Ensemble Seasonal Streamflow Forecasts in the Upper Colorado River Basin Using Multi‐Model Climate Forecasts

AbstractIn the Colorado River Basin (CRB), ensemble streamflow prediction (ESP) forecasts drive operational planning models that project future reservoir system conditions. CRB operational seasonal streamflow forecasts are produced using ESP, which represents climate using an ensemble of meteorological sequences of historical temperature and precipitation, but do not typically leverage additional real‐time subseasonal‐to‐seasonal climate forecasts. Any improvements to streamflow forecasts would help stakeholders who depend on operational projections for decision making. We explore incorporating climate forecasts into ESP through variations on an ESP trace weighting approach, focusing on Colorado River unregulated inflows forecasts to Lake Powell. The k‐nearest neighbors (kNN) technique is employed using North American Multi‐Model Ensemble one‐ and three‐month temperature and precipitation forecasts, and preceding three‐month historical streamflow, as weighting factors. The benefit of disaggregated climate forecast information is assessed through the comparison of two kNN weighting strategies; a basin‐wide kNN uses the same ESP weights over the entire basin, and a disaggregated‐basin kNN applies ESP weights separately to four subbasins. We find in general that climate‐informed forecasts add greater marginal skill in late winter and early spring, and that more spatially granular disaggregated‐basin use of climate forecasts slightly improves skill over the basin‐wide method at most lead times.

Integrated operational planning model, considering optimal delivery routing, incentives and electric vehicle aggregated demand management

The use of electric vehicles is being promoted to address emerging concerns about global warming associated with emissions from fossil fuels. Besides, in the context of parcel delivery deep growth related to e-commerce, electric vehicle is becoming an alternative to conventional fossil fuel technology. Intrinsically, the charging process implies the interdependence between the transportation and electric power systems. This paper presents a new multistage optimization-based approach that allows linking delivery routing and aggregated demand management in the transportation and electric power systems, respectively. For the routing and charging of each independent electric vehicle, battery degradation, acceleration- and speed-dependent power consumption, penalty for delivery delay, tolls, fixed charging prices and incentives for availability time are considered. An electric vehicle demand aggregator is used to guarantee the synergy between systems. Incentives are included to motivate electric vehicles to remain at charging intersections. However, attractive incentives can create electric power system congestion due to simultaneous charges on nodes. Thus, an iterative decongestion methodology is developed. The resulting model is divided into three stages: delivery allocation, delivery routing for each independent electric vehicle and optimal energy management by electric vehicle demand aggregator. The resulting optimization problem is cast as a mixed-integer linear programming model for the first two stages and a linear programming model for the third stage. Numerical results demonstrate the effectiveness of the proposed model on a real 284-intersection map with a set of 100 electric vehicles, showing that incentives allow electric vehicle demand aggregator to achieve cost savings of 8.5%.

Techno-economic feasibility analysis of a eucalyptus-based power plant using woodchips

Forest biomass plays a critical role in developing countries, with high potential to contribute to the energy needs as a cheaper and carbon-friendly alternative. In Brazil, eucalyptus plantations have gained attention in recent years as a promising source for the power sector. Aiming to assess this potential, this work analyzed the techno-economic feasibility of a eucalyptus woodchip-based power plant. A typical 10 MW power plant was modeled using empirical parameters based on the Steam Rankine Cycle. Woodchip samples were collected and energy properties, fuel demand, eucalyptus area required and unit variable cost were determined. In economic analysis, NEWAVE (Long and Medium Term Interconnected Hydrothermal Systems Operation Planning Model) and ANAFIN (Financial Analysis Model for Electrical Projects), both developed by CEPEL (Brazilian Electrical Research Center) were used for the first time to analyze forest biomass projects taking into account fuel availability and hydrological risks. Three energy trading scenarios were evaluated: (1) full commercialization in Brazilian regulated market, (2) full in free market and (3) partial in both markets. Results showed that 4,200 ha of eucalyptus plantations would be needed to ensure sustainable woodchip supply. Moisture content, basic density and woodchip price played an essential role in determining future costs. All scenarios of energy trading were economic feasible, and scenario (3) mitigated energy spot price risk and might be considered the effective risk-return strategy. The simulated strategies may improve decision makers visibility over the risk-return relation of a eucalyptus power plant. This methodology may be used to evaluate new forest biomass energy projects in potential areas. This study will help encourage bioenergy and power diversification in developing countries.

A stochastic operational planning model for a zero emission building with emission compensation

The primary objective of Zero Emission Buildings (ZEBs) is to achieve net zero emission over the buildings’ lifetime. To achieve this goal, accurate cost-effective emission compensation is needed during the operational phase. This paper presents a stochastic planning model comprising an emission inventory for the operation of ZEBs. The operational planning methodology uses stochastic dynamic programming (SDP) to analyze and represent the expected future cost curve (EFCC) for operation based on the electricity price and accumulated CO2eq-inventory during the year. Failing to compensate for net zero emission makes the leftover amount subject to a penalty cost at the end of the year. This renders the overall problem multi-objective optimization including emission compensation and cost of operation. The model is applied to a case study of a Norwegian building, tested for a range of penalty costs for leftover CO2eq-inventory. The results show that, for a ZEB, including emission compensation demonstrates a significant impact on the operation of the building. The penalty cost puts a limit on how much the operational cost increase for additional compensation should be, influencing the end CO2eq-inventory. Increasing penalty costs decreases the end inventory, and a penalty cost of 10 EURkgCO2eq resulted in zero emission. The case achieving zero emission had an operational cost increase of 4.8% compared to operating without a penalty cost. This shows the importance of accounting for emissions during the operation of a ZEB, and the value of having an operational strategy that presents the future impact of operation.

Different Strategic Planning Models: Considerations for International Study Programs

Multiple different strategic and operational planning models are provided that may constructively impact international study planning within an educational institution. The models covered include: Porter's Five Forces, SWOT analysis, VRIO framework, Value Chair Analysis (VCA), and PESTEL analysis.