Proposed Planning Approach Research Articles

Accelerating the Transition to sustainable energy: An intelligent decision support system for generation expansion planning with renewables

Sustainable energy planning for power generation with renewables requires a multidimensional approach since it directly impacts several sectors such as the national grid, the environment, and the economy. This paper suggests an intelligent decision based on a combination of the Analytical Hierarchy Process and Artificial Neural Networks to assist policymakers in evaluating future renewable energy investments for power generation. A case study of Bahrain's power system is used to illustrate the usefulness of the proposed planning approach and also to discuss its efficiency. The Analytical Hierarchy Process model outcomes revealed that wind turbines are the most appropriate technology for Bahrain with 32.5 % priority, followed by PV and CSP with 32.2 % and 16.3 %, respectively. Then, the Artificial Neural Networks model was structured based on the generated scenarios from the Analytical Hierarchy Process model, and it reached its best performance after 384 cycles. The integrated approach overcomes some of the Analytical Hierarchy Process's limitations and provides an intelligent tool for advanced assessment of a sustainable power system.

A flexible planning approach for integrated lot sizing and rework planning with random proportion of defective products

We consider a stochastic capacitated lot sizing problem with in-line rework. Both defective and defect-free products can be the result of an imperfect production process in real-world production systems. However, the proportion of defective items in a production lot is subject to uncertainty. For economic and environmental reasons, the possibility to rework defective products appears reasonable since these products usually have considerable value. In this paper, a nonlinear model formulation for integrated lot sizing and rework planning is proposed. We use a sample average approach to approximate the generic nonlinear model formulation. To cope with the randomness of the proportion of defective products, we apply a flexible planning approach that allows the adjustment of production and rework quantities. We conduct extensive numerical investigations to evaluate the performance of the proposed planning approach.

Distribution network planning method: Integration of a recurrent neural network model for the prediction of scenarios

The integration of new types of consumers and prosumers into distribution networks presents significant challenges for network planners, especially given the uncertainties of future expansions. This paper introduces a method for distribution network planning that integrates a Long-Short Term Memory (LSTM) model with a confidence interval threshold for long-term scenario predictions. The proposed forecasting model considers datasets collected from monitoring systems which include aggregated demand and self-consumption from photovoltaic sources. The use of the LSTM model enables a more accurate prediction of expansions, as it captures nonlinear patterns in time series data while taking into account the inherent characteristics of non-stationary time series data. The proposed method was tested on an existing radial medium voltage distribution network in Spain, taking into account the operational thermal limits for lines and substation time series measurements. The proposed planning approach also considers asset costs, active planning solutions, and time-series load flow analysis. The results obtained indicate that the use of time-based forecasts from aggregated generation and demand is a more realistic solution for flexible planning solutions when they are compared to traditional strategies.

Synchromodal freight transport re-planning under service time uncertainty: An online model-assisted reinforcement learning

The objective of this study is to address the issue of service time uncertainty in synchromodal freight transport, which can cause delays, inefficiencies, and reduced satisfaction for shippers. The proposed solution is an online deep Reinforcement Learning (RL) approach that takes into account the service time uncertainty, assisted by an Adaptive Large Neighborhood Search (ALNS) heuristic that provides state and reward information based on the routing and scheduling. The proposed planning approach re-plans in response to unexpected events and learns from real-time information from various transport modes, including road, railway, and inland waterways. The performance of the proposed planning approach is evaluated in the European Rhine-Alpine corridor under various scenarios with different types and severities of unexpected events. The results demonstrate that the RL approach consistently outperforms other strategies by effectively handling service time uncertainty, leading to reduced costs, emissions, and waiting time, as well as decreased transport delays and improved rewards through accurate decision-making and agile transport re-planning. This study also finds that incorporating event severity information improves the average reward obtained by the RL approach in scenarios involving multiple types of events.

Defining Natural Habitat Types as Nature-Based Solutions in Urban Planning

This study focuses on solving urban challenges, such as temperature reduction, urban stormwater management, noise reduction, air quality improvement, and CO2 concentration reduction, and suggests terrestrial and freshwater habitat types (HTs) found in Europe as innovative forms of nature-based solutions (NBSs). Establishing native HTs in various urban environments to solve urban challenges would enhance biodiversity at different levels and integrate this aspect into urban planning. This contribution builds on the recognition that vegetated surfaces are the most versatile NBS for addressing the broadest range of environmental problems in urban areas and on the understanding that the processes running within these green spaces offer the key to socio-ecological improvements of such areas. Employing a narrative literature review, qualitative content analysis, and interdisciplinary expert discussion, this paper defines why and how unaltered native HTs can be implemented as NBSs in the urban environment, indicates potential HTs for specific urban challenges, and presents an approach to the inclusion of HTs as NBSs in spatial planning documents at national, regional, and local levels. The proposed planning approach attributes added value to HTs and, by linking the concepts of NBSs and HTs, integrates them into urban planning.

Double-layer Planning of Distribution Network considering Flexible Supply and Demand

To improve the flexibility of the system, flexible resources are added to the planning of renewable energy systems for collaborative optimization. Firstly, three quantitative indicators of flexible resources are introduced to build a distributed double-layer distribution network planning model that considers flexible resources and a more detailed cost model is considered based on existing research. Secondly, the scheduling and optimization of controllable loads are coordinated to achieve a fully integrated configuration of the distribution network. Then, the APSO algorithm is used to solve the problem. Finally, test results verify that the proposed planning approach is effective in improving system flexibility, economy, and reliability.

A decision-centric approach for techno-economic optimization and environmental assessment of standalone and grid-integrated renewable-powered electric vehicle charging stations under multiple planning horizons

The automotive sector is undergoing a transitional phase towards electrification with the goal of achieving climate sustainability. Renewable-powered electric vehicle (EV) charging infrastructure can play a vital role in this paradigm shift. To support and provide a roadmap to this transition, the current study aims to investigate a sustainable planning approach for optimal renewable-powered electric vehicle charging stations (EVCS) within institutional premises. A feasibility analysis for multiple renewable-based EV charging station configurations has been carried out based on multi-objective techno-economic and environmental indicators including Levelized cost of energy (LCOE), Net present cost (NPC), and carbon emissions. The proposed planning approach has been applied under Fast charging for private vehicles and on-board charging for Ez-bikes and shuttles (FCOBC) and Medium- charging for private vehicles and on-board charging for Ez-bikes and shuttles (MCOBC). Results reveal that a PV system backed with a utility grid enabling both energy purchase and sale during peak hours and off-peak hours respectively remained the most feasible configuration for EV charging infrastructure experiencing an LCOE of $0.1289/kWh to $0.1262/kWh and $0.1168/kWh to $0.1161/kWh for both FCOBC and MCOBC, respectively. Along with the environmental analysis, a forest absorbing carbon emissions analysis has been performed to provide a policy optimization for the future environmental outlook. Results validations have been undertaken for levelized average tariffs of single point supply (above 66 kV) of different government-owned distribution companies, which indicated the commercial efficacy of the proposed EV charging infrastructure, where the LCOE is lower than the real-time operational tariffs within the region. Finally, a sensitivity analysis is carried out under different uncertainties to check the optimization behavior of proposed systems.

Optimal allocation of FCSs and PV units in smart grids considering traffic and generation dispatch

Worldwide, transportation electrification and photovoltaics (PVs) have become vital components of modern and future planning problems. Therefore, the large-scale deployment of electric vehicles (EVs) requires reliable fast-charging stations (FCSs). However, random allocation of FCSs and PV adversely impacts the performance and operation of the traffic flow and power networks. Thus, this paper proposes a new planning approach for the optimal allocation of FCSs and PVs in large-scale smart grids and transportation networks. Unlike existing approaches, the well-known Dynamic Network Assignment Simulation for Road Telematics (DYNASMART) is updated and employed in the proposed planning approach to accurately simulate the EVs within the traffic stream. The average waiting time of the EVs at the charging stations (including charging time) and the total annual costs are utilized as conflicting objective functions to be minimized. To effectively solve this comprehensive model with conflicting objectives, a bi-layer multiobjective optimization based on metaheuristic (NSGA-II) and deterministic (GAMS) algorithms is established. The outer layer optimizes the allocation of FCSs and PV accurately, while the inner one optimally dispatches the generated power of the conventional generation units. The proposed planning approach is tested on a 26-bus electrical transmission network and 92-node transportation system. The simulation results demonstrate the efficiency of the proposed approach. The optimal allocation of FCSs and PVs with 80% EVs penetration increases the net profit by 85.36% while maintaining a maximum of 30 minutes for both charging and waiting time.

Artificial Potential Field Incorporated Deep-Q-Network Algorithm for Mobile Robot Path Prediction

Autonomous navigation of mobile robots is a challenging task that requires them to travel from their initial position to their destination without collision in an environment. Reinforcement Learning methods enable a state action function in mobile robots suited to their environment. During trial-and-error interaction with its surroundings, it helps a robot to find an ideal behavior on its own. The Deep Q Network (DQN) algorithm is used in TurtleBot 3 (TB3) to achieve the goal by successfully avoiding the obstacles. But it requires a large number of training iterations. This research mainly focuses on a mobility robot’s best path prediction utilizing DQN and the Artificial Potential Field (APF) algorithms. First, a TB3 Waffle Pi DQN is built and trained to reach the goal. Then the APF shortest path algorithm is incorporated into the DQN algorithm. The proposed planning approach is compared with the standard DQN method in a virtual environment based on the Robot Operation System (ROS). The results from the simulation show that the combination is effective for DQN and APF gives a better optimal path and takes less time when compared to the conventional DQN algorithm. The performance improvement rate of the proposed DQN + APF in comparison with DQN in terms of the number of successful targets is attained by 88%. The performance of the proposed DQN + APF in comparison with DQN in terms of average time is achieved by 0.331 s. The performance of the proposed DQN + APF in comparison with DQN average rewards in which the positive goal is attained by 85% and the negative goal is attained by −90%.

Multi‐objective optimal planning of EV charging stations and renewable energy resources for smart microgrids

AbstractDistribution system planners and operators have increasingly exposed great attention to maximizing the penetration of renewable energy resources (RERs), and electric vehicles (EVs) toward modern microgrids. Accordingly, intensive operational and economic problems are expected in such microgrids. Specifically, the operators need to meet the increased demand for EVs and increase the dependence on RERs. The charging strategy for EVs and the RER penetration level may result in increased power loss, thermal loading, voltage deviation, and overall system cost. To address these concerns, this paper proposed an optimal planning approach for allocating EV charging stations with controllable charging and hybrid RERs within multi‐microgrids, where the charging strategy in the proposed planning approach contributed to improving power quality and overall system cost, where the voltage deviation, energy not supplied, total cost have been reduced to 26.03%, 49.57%, and 70.45%, respectively. The simulation results are compared with different optimization techniques to verify the effectiveness of the proposed algorithm. The proposed simultaneous allocation approach of EV charging stations and RERs can reduce operating costs for RERs and conventional stations while increasing the charging stations' capacity.

Allocation of battery energy storage in power distribution network: Placement, sizing, and scheduling

ABSTRACT The present power distribution infrastructure faces significantly high demand in most of the time hours of the day. Thus, it needs to manage power intelligently in those high-demand hours. Optimal allocation of battery energy storage system (BESS) can foster power management in the distribution system by exchanging energy with it. However, BESS should be strategically allocated to achieve maximum power delivery efficiency with lower investment. BESS units also need to be strategically scheduled to charge in light load hours and discharged in peak demand hours. The present study proposes a strategic planning model that can minimize the cost of BESS allocation and improve the distribution network’s technical performance. The cost function is framed considering battery life, investment in BESS, and investment in the inverter. The distribution system’s performance is realized with a typical network performance index (NPI). The NPI is synthesized from voltage deviation of buses and change in total active power losses in the network. A multiobjective optimization algorithm-based solution technique is presented to comprehend the planning model’s outcome in the most suitable location, size, and battery scheduling scenario. The proposed planning approach has been tested on a 28-bus Indian power distribution network. The result of techno-economic optimization confirms a good trade-off solution with 37.5% of network performance improvement with respect to the base configuration and INR 39.06LAC of total investment. The proposed approach has been verified with the existing BESS allocation model, demonstrating its efficacy.

Sine Resistance Network-Based Motion Planning Approach for Autonomous Electric Vehicles in Dynamic Environments

This article proposes a motion planning approach for autonomous electric vehicles to generate an appropriate planned path according to the time-varying surrounding information. This approach utilizes the proposed novel sine resistance network to mesh the road with the aim of improving the planned path smoothness, which has the capability of generating a continuous-curvature planned path that contributes to tracking and reducing the jerkiness. Meanwhile, considering that the classical artificial potential field (APF) method is only suitable for the static scenarios, a bias oval APF is constructed to predict the change of relative distance between the ego vehicle and each obstacle by taking the speed information into account. The proposed planning approach can ensure that the planned path is collision-free in dynamic environments and the generated path is smooth simultaneously. Cosimulation results in CarSim and MATLAB/Simulink are provided to prove the advantage and feasibility of the proposed motion planning approach for autonomous electric vehicles.

Security-constrained AC–DC hybrid distribution system expansion planning with high penetration of renewable energy

With the increasing penetration of renewables into the AC–DC hybrid distribution system, it is necessary to develop a security-constrained planning approach to address the uncertainty brought by renewables. Motivated by the urgent need to accommodate high penetration of renewable energy resources in weak grids, this paper presents a novel planning approach for AC–DC hybrid distribution systems considering the N-1 security criterion. Network reconfiguration is incorporated as a method to improve the flexibility of the system under normal and post-contingency conditions. To alleviate the burden of computing all possible N-1 contingencies, a novel solving strategy with a nested loop structure is proposed. In the inner loop, a novel contingency screening method is designed for the fast identification of high-risk contingencies under a given planning solution. In the outer loop, the obtained high-risk contingency set is adopted to refine and filter the security constraints, so that the planning solution can be improved iteratively. The superiority and effectiveness of the proposed planning model and solving strategy are tested on a real-world distribution network in Zhejiang Province, China. Numerical results verify that the proposed planning approach is able to deal with the installation of large-scale renewable energy while ensuring the security of the system.

An Enhanced MILP Model for Multistage Reliability-Constrained Distribution Network Expansion Planning

Reliability is an essential factor in distribution networkt expansion planning. However, standard distribution reliability assessment techniques rely on quantifying the impact of a pre-specified set of events on service continuity through the simulation of component outages, one at a time. Due to such a simulation-based nature, the incorporation of reliability into distribution network expansion planning has customarily required the application of heuristic and metaheuristic approaches. Recently, alternative mixed-integer linear programming (MILP) models have been proposed for distribution network expansion planning considering reliability. Nonetheless, such models suffer from either low computational efficiency or over-simplification. To overcome these shortcomings, this paper proposes an enhanced MILP model for multistage reliability-constrained distribution network expansion planning. Leveraging an efficient, yet accurate reliability evaluation model, proposing a customized technique for effectively imposing radial operation, as well as utilizing pragmatic measures to model reliability-related costs are the salient features of this work. In this respect, practical reliability-related costs are considered based on reliability incentive schemes and the revenue lost due to undelivered energy during customer outages. The proposed planning approach is tested on four networks with 24, 54, 86, and 138 nodes to illustrate its efficiency and applicability.

Planning for Autonomous Driving via Interaction-Aware Probabilistic Action Policies

Devising planning algorithms for autonomous driving is non-trivial due to the presence of complex and uncertain interaction dynamics between road users. In this paper, we introduce a planning framework encompassing multiple action policies that are learned jointly from episodes of human-human interactions in naturalistic driving. The policy model is composed of encoder-decoder recurrent neural networks for modeling the sequential nature of interactions and mixture density networks for characterizing the probability distributions over driver actions. The model is used to simultaneously generate a finite set of context-dependent candidate plans for an autonomous agent and to anticipate the probable future plans of human drivers. This is followed by an evaluation stage to select the agent plan with the highest expected utility for execution. Our approach leverages rapid sampling of action distributions in parallel on a graphic processing unit, offering fast computation even when modeling the interactions among multiple vehicles and over several time steps. We present ablation experiments and comparison with two existing baseline methods to highlight several design choices that we found to be essential to our model’s success.We test the proposed planning approach in a simulated highway driving environment, showing that by using the model, the agent can plan actions that mimic the interactive behavior of humans.

A DfX-based approach for incorporating sustainability in infrastructure project planning

PurposeThis paper investigates how the “Design for Sustainability” mindset of the manufacturing sector can be effectively transferred in the context of construction and proposes appropriate implementation tools for the promotion of sustainability in infrastructure projects' planning and design.Design/methodology/approachThe Design for Excellence (DfX) approach is first investigated in the context of manufacturing through the review of related literature, and the goals of Design for Sustainability (DfS), in particular, are specified. The suggestions of the literature are used as a basis for the development of the conceptually equivalent goals in the context of infrastructure projects and to highlight the construction management strategies which qualify as potential DfS tools.FindingsThis paper presents how well-known construction planning and management methods, techniques or strategies such as cost–benefit analysis, sustainability-driven procurement, contractors' involvement in design, value-based design, building information modeling (BIM) and buildability review enable the holistic promotion of the triple bottom line in feasibility, design and procurement of infrastructure projects, when combined in the context of an integrated planning approach.Originality/valueThe DfX mindset has very limited presence in the construction literature. This paper attempts to conceptualize its scope in the context of infrastructure projects while putting the multidimensional concept of sustainability as primary planning and design consideration. The proposed planning approach and the systematic mapping of the respective implementation tools can facilitate the decision-makers to achieve the desired sustainability benefits, in line with their priorities.

Optimal sizing and location of open-UPQC in distribution networks considering load growth

Voltage and current deficiencies in distribution network (DN) necessitate the improvement of power quality (PQ). Custom power devices (CPDs) compensate for these deficiencies. Recently, in presence of communication infrastructure in smart distribution network, open unified power quality conditioner (OUPQC) is attracted the attention of DN researchers and operators. OUPQC is utilized in DN to improve voltage and current deficiencies by operation of its series unit (SEU) and shunt units (SHU), respectively. Simultaneous compensation capability of voltage and current in distribution network increase its utilization by network operator. In steady state condition, the operation of SHUs compensate the reactive power flow in distribution network and the operation of SEU improve the voltage deficiencies in installation location. Hence, the optimum utilization of OUPQC result in maximization of its benefit for voltage and current improvement. In this regard, the planning of distribution network is performed considering the utilization of OUPQC and load growth. The optimum location, size and installation time of OUPQC is determined for minimizing the cost of distribution network during the planning horizon. Power loss reduction and voltage profile improvement are considered as benefits of the proposed planning approach. On the other hand, the added costs to the distribution network include the installation and maintenance costs of SEU and SHUs of OUPQC. The proposed planning approach is utilized to be tested on both standard IEEE 69-bus and practical Iranian 95-bus distribution networks. The results show that the proposed planning method is effective in term of economic and technical parameters of distribution networks and is useful for network planners.

Flexible expansion planning of distribution system integrating multiple renewable energy sources: An approximate dynamic programming approach

Distribution system expansion planning is a long-term process with sequential multistage uncertain factors, so flexible planning methods are required to deal with the various potential investment risks. This paper presents an approximate dynamic programming based flexible distribution system expansion planning model, in which the long-term system load growth uncertainty and short-term power fluctuation are both considered. A flexible investment strategy based on Markov decision process is developed, in which the planning decisions are made sequentially with consideration of both the current costs and future variable costs under long-term load growth uncertainty. This problem is formulated as a large-scale multistage stochastic programming model, which is intractable due to the “curse of dimensionality”. Thus, an approximate dynamic programming approach is used to decompose the original multistage optimization problem into sequential subproblems that can be easily solved. Case studies are carried out on a 24-node distribution system with three planning stages and a 54-node distribution system with five planning stages. Numerical results validate the feasibility and benefits of the proposed planning approach in obtaining a flexible expansion scheme, which can significantly reduce the investment risk and configure renewable energy equipment more reasonably.

Step-wise stowage planning of roll-on roll-off ships transporting dangerous goods

Planning stowage with the presence of dangerous goods is critical to ensure safety at sea. In this paper, we propose a step-wise stowage planning approach to generate optimal stowage plans for roll-on roll-off ships transporting trailers (some containing dangerous cargo) between two ports. The planning approach consists of three steps, where Step 1 maximizes the number of dangerous cargo units to transport while adhering to the International Maritime Dangerous Goods regulations. Step 2, which is optional, maximizes the safety distance among the dangerous cargo units found in the first step. Finally, in Step 3, the ballast water intake needed to ensure stability of the ship is minimized, as this has a significant effect on the fuel consumption. Computational results on instances generated based on real data from a shipping company show that the proposed planning approach might both reduce the ballast water intake (and hence reduce the fuel consumption) and increase the safety distance among dangerous cargo units.

Evolutionary Algorithms for 5G Multi-Tier Radio Access Network Planning

With the ever-increasing traffic demand of wireless users, resulting from the huge deployment of Internet-of-Things (IoT) devices and the emergence of smart city applications requiring ultra-low latency networks, the Fifth Generation (5G) of cellular networks have been introduced as a revolutionary broadband technology to boost the quality of service of mobile users. In this paper, we investigate the planning process for a 5G radio access network having mmWave Micro Remote Radio Units (mRRUs) on top of sub-6 GHz Macro Remote Radio Units (MRRUs). We rely on proper channel models and link budgets as well as Urban Macro-cells (UMa) and Urban Micro-cells (UMi) characteristics to carefully formulate a 5G network planning optimization problem. We aim to jointly determine the minimum number of MRRUs and mRRUs to install and find their locations in a given geographical area while fulfilling coverage and user traffic demand constraints. In order to solve this planning process, we propose a two-step process where we first employ a low complexity meta-heuristic algorithm to optimize the locations of RRUs followed by an iterative elimination method to remove redundant cells. To evaluate the performances of this proposed approach, we conduct a comparative study using Accelerated Particle Swarm Optimization and Simulated Annealing. Simulations results using sub-6 GHz UMa and 28 GHz UMi demonstrate the ability of the proposed planning approach to achieve more than 98% coverage with minimum cell capacity outage rate, not exceeding the 2%, for different scenarios and illustrate the efficiency of the evolutionary algorithms in solving this NP-hard problem in reasonable running time.