Energy transitions around the world will change the spatial fingerprint of the electricity sector, but there is a lack of studies on citizen preferences for siting the future mix of electricity technologies. Using the case of Switzerland in 2035, we present a serious board game to form and elicit citizen preferences for spatial siting of a full mix of electricity technologies and we test this game with 44 participants in the city of Zurich. The game proves to help elicit valid preferences of the participants and lead to measurable learning effects about this complex, multi-dimensional topic. The results show that these 44 participants prefer a diverse mix of renewable technologies for Switzerland in 2035. In terms of siting, these participants consistently choose the efficiency strategy, where new plants are concentrated in the areas where they produce most electricity at least cost, in contrast to the strategy of regional equity, where all Swiss regions would equally build new generat... [more]

The industry 4.0 concepts are moving towards flexible and energy efficient factories. Major flexible production lines use battery-based automated guided vehicles (AGVs) to optimize their handling processes. However, optimal AGV battery management can significantly shorten lead times. In this paper, we address the scheduling problem in an AGV-based job-shop manufacturing facility. The considered schedule concerns three strands: jobs affecting machines, product transport tasks’ allocations and AGV fleet battery management. The proposed model supports outcomes expected from Industry 4.0 by increasing productivity through completion time minimization and optimizing energy by managing battery replenishment. Experimental tests were conducted on extended benchmark literature instances to evaluate the efficiency of the proposed approach.

Integrating active power curtailment (APC) of renewable energy sources (RES) in power system planning reduces necessary investments in the power system infrastructure. In current target grid planning methods, APC is considered by fixed curtailment factors without considering the provided flexibility to its full extent. Time-series-based planning methods allow the integration of the time dependency of RES and loads in power system planning, leading to substantial cost savings compared to the worst-case method. In this paper, we present a multi-year planning strategy for high-voltage power system planning, considering APC as an alternative investment option to conventional planning measures. A decomposed approach is chosen to consider APC and conventional measures in a long-term planning horizon of several years. The optimal investment path is obtained with the discounted cash flow method. A case study is conducted for the SimBench high-voltage urban benchmark system. Results show that t... [more]

The uncertainty of generation and load increases in the transmission network in the power market. Considering the transmission congestion risk caused by various uncertainties of the transmission network, the optimal operation strategies of the transmission network under various operational scenarios are decided, aiming for the maximum of social benefit for the evaluation of the degree of scenario congestion. Then, a screening method for major congestion scenario is proposed based on the shadow price theory. With the goal of maximizing the difference between the social benefits and the investment and maintenance costs of transmission lines under major congestion scenarios, a multi-stage transmission network planning model based on major congestion scenarios is proposed to determine the configuration of transmission lines in each planning stage. In this paper, the multi-stage transmission network planning is a mixed integer linear programming problem. The DC power flow model and the comm... [more]

This paper proposes a method of determining the optimal tap settings for no-load distribution transformers with tap-changing capabilities that is practical to apply in real distribution networks. The risk of low voltage distribution networks violating voltage constraints is impacted by the increasing uptake of distributed energy resources and embedded generation. Some of this risk can be alleviated by suitably setting no-load transformer tap settings, however, modifying these taps requires customer outages and must be infrequent. Hence, loading over the entire year must be considered to account for seasonal variations when setting these taps optimally. These settings are determined using evolution strategy optimisation based on an average loading case. Monte Carlo simulations are used to calculate the probability that the terminal voltages on the distribution transformer secondary terminals violate the network voltage limits when the optimal set of taps for the average case is applied... [more]

Transmission system operator (TSOs) need to project the system state at the seasonal scale to evaluate the risk of supply-demand imbalance for the season to come. Seasonal planning of the electricity system is currently mainly adressed using climatological approach to handle variability of consumption and production. Our study addresses the need for quantitative measures of the risk of supply-demand imbalance, exploring the use of sub-seasonal to seasonal forecasts which have hitherto not been exploited for this purpose. In this study, the risk of supply-demand imbalance is defined using exclusively the wind energy production and the consumption peak at 7 pm. To forecast the risks of supply-demand imbalance at monthly to seasonal time horizons, a statistical model is developed to reconstruct the joint probability of consumption and production. It is based on a the conditional probability of production and consumption with respect to indexes obtained from a linear regression of principa... [more]

In this paper, the proposed methodology minimizes the electricity cost of a laundry room by means of load shifting. The laundry room is equipped with washing machines, dryers, and irons. Additionally, the optimization model handles demand response signals, respecting user preferences while providing the required demand reduction. The sequence of devices operation is also modeled, ensuring correct operation cycles of different types of devices which are not allowed to overlap or have sequence rules. The implemented demand response program specifies a power consumption limit in each period and offers discounts for energy prices as incentives. In addition, users can define the required number of operations for each device in specific periods, and the preferences regarding the operation of consecutive days. In the case study, results have been obtained regarding six scenarios that have been defined to survey about effects of different energy tariffs, power limitations, and incentives, in a... [more]

Recent developments, such as smart metering, distributed energy resources, microgrids, and energy storage, have led to an exponential increase in system complexity and have emphasized the need to include customer behavior and social and cultural backgrounds in planning activities. This paper analyzes how emergent behavior in electricity consumption can affect the planning of distribution grids with a smart grid vision. For this, an agent-based model that uses insights from the field of behavioral economics to differentiate four consumer categories (high income, low income, middle class, and early adopters) was used. The model was coupled with a real distribution feeder and customer load curve data, and the results showed that heterogeneity of customer’s preferences, values, and behavior led to very distinct load growth patterns. The results emphasize the relevance of modeling customer’s behavioral aspects in planning increasingly complex power systems.

In the context of climate changes and the rapid growth of energy consumption, intermittent renewable energy sources (RES) are being predominantly installed in power systems. It has been largely elucidated that challenges that RES present to the system can be mitigated with energy storage systems (ESS). However, besides providing flexibility to intermittent RES, ESS have other sources of revenue, such as price arbitrage in the markets, balancing services, and reducing the cost of electricity procurement to end consumers. In order to operate the ESS in the most profitable way, it is often necessary to make optimal siting and sizing decisions, and to determine optimal ways for the ESS to participate in a variety of energy and ancillary service markets. As a result, many publications on ESS models with various goals and operating environments are available. This paper aims at presenting the results of these papers in a structured way. A standard ESS model is first outlined, and that is fol... [more]

This paper presents a gain scheduling output feedback control method to reduce driver workload and improve driving performance by considering input saturation. The driver−vehicle system model is developed by considering tire cornering stiffness uncertainties and different driver parameter uncertainties. Meanwhile, the input saturation is also considered in the driver-vehicle system. A quadratic Lyapunov function is designed to solve the optimization problem with uncertainties and input saturation. The results, which are based on the MATLAB-CarSim co-simulation platform, indicate that the robust controller not only improves the convergence rate of the state but also reduces the steering workload of the driver.

Smart microgrids (SMGs) may face energy rationing due to unavailability of energy resources. Demand response (DR) in SMGs is useful not only in emergencies, since load cuts might be planned with a reduction in consumption but also in normal operation. SMG energy resources include storage systems, dispatchable units, and resources with uncertainty, such as residential demand, renewable generation, electric vehicle traffic, and electricity markets. An aggregator can optimize the scheduling of these resources, however, load demand can completely curtail until being neglected to increase the profits. The DR function (DRF) is developed as a constraint of minimum size to supply the demand and contributes solving of the 0-1 knapsack problem (KP), which involves a combinatorial optimization. The 0-1 KP stores limited energy capacity and is successful in disconnecting loads. Both constraints, the 0-1 KP and DRF, are compared in the ranking index, load reduction percentage, and execution time. B... [more]

Multi-microgrids address the need for a resilient, sustainable, and cost-effective electricity supply by providing a coordinated operation of individual networks. Due to local generation, dynamic network topologies, and islanding capabilities of hosted microgrids or groups thereof, various new fault mitigation and optimization options emerge. However, with the great flexibility, new challenges such as complex failure modes that need to be considered for a resilient operation, appear. This work systematically reviews scheduling approaches which significantly influence the feasibility of mitigation options before a failure is encountered. An in-depth analysis of identified key contributions covers aspects such as the mathematical apparatus, failure models and validation to highlight the current methodical spectrum and to identify future perspectives. Despite the common optimization-based framework, a broad variety of scheduling approaches is revealed. However, none of the key contributio... [more]

Efficient big data analysis is critical to support applications or services in Internet of Things (IoT) system, especially for the time-intensive services. Hence, the data center may host heterogeneous big data analysis tasks for multiple IoT systems. It is a challenging problem since the data centers usually need to schedule a large number of periodic or online tasks in a short time. In this paper, we investigate the heterogeneous task scheduling problem to reduce the global task execution time, which is also an efficient method to reduce energy consumption for data centers. We establish the task execution for heterogeneous tasks respectively based on the data locality feature, which also indicate the relationship among the tasks, data blocks and servers. We propose a heterogeneous task scheduling algorithm with data migration. The core idea of the algorithm is to maximize the efficiency by comparing the cost between remote task execution and data migration, which could improve the da... [more]

The goal of this paper was to evaluate heat loss and the demand of district heating (DH) in the context of the fourth generation DH concept using a data-driven approach. The heat loss profile was calculated with GIS Zulu© (software (8.0.0.7539, Politerm, LLC, St.Petersburg, Russia) using eight various states of insulation, detailed information on thermal conductivity, internal heat transfer coefficient, and geometry of the concrete trench. There is a strong correlation between the heat sold and the average annual outdoor temperatures. The outstanding episodes are extremely rare, and the difference in the overall pattern is elusive. The results of the annual heat production and annual heat loss analyses were compared using three different estimation methods. The new method was the only one that showed a positive effect after the complete modernization of thermal insulation. The actual proportion of heat loss is much higher at 16%, while the actual heat delivery is less than anticipated... [more]

Mixed Integer Linear Programming (MILP) optimization algorithms provide accurate and clear solutions for Microgrid and Distributed Energy Resources projects. Full-scale optimization approaches optimize all time-steps of data sets (e.g., 8760 time-step and higher resolutions), incurring extreme and unpredictable run-times, often prohibiting such approaches for effective Microgrid designs. To reduce run-times down-sampling approaches exist. Given that the literature evaluates the full-scale and down-sampling approaches only for limited numbers of case studies, there is a lack of a more comprehensive study involving multiple Microgrids. This paper closes this gap by comparing results and run-times of a full-scale 8760 h time-series MILP to a peak preserving day-type MILP for 13 real Microgrid projects. The day-type approach reduces the computational time between 85% and almost 100% (from 2 h computational time to less than 1 min). At the same time the day-type approach keeps the objective... [more]

The number of plug-in electric vehicles (PEVs) has rapidly increased owing to the government’s active promotion policy worldwide. Consequently, in the near future, their charging demand is expected to grow enough for consideration in the planning process of the transmission system. This study proposes a stochastic method for modeling the PEV charging demand, of which the time and amount are uncertain. In the proposed method, the distribution of PEVs is estimated by the substations based on the number of electricity customers, PEV expansion target, and statistics of existing vehicles. An individual PEV charging profile is modeled using the statistics of internal combustion engine (ICE) vehicles driving and by aggregating the PEV charging profiles per 154 kV substation, the charging demand of PEVs is determined for consideration as part of the total electricity demand in the planning process of transmission systems. The effectiveness of the proposed method is verified through case studie... [more]

This article assesses the costs and benefits of incorporating battery energy storage systems (BESS) in transmission network expansion planning (TEP) over multiple time periods. We propose a mixed-integer programming model (MIP) for joint planning of the installation of battery energy storage systems (BESS) and construction of new transmission lines in multiple periods of time. The mathematical formulation of the presented model is based on the strategies of the agents of a transmission network to maximize their benefit, and on the operational restrictions of the power flows in transmission networks. This analysis is performed for the Garver 6 node test system takes into account the power losses in the lines and the restrictions for the energy stored in BESS. The power flows obtained with the MIP model are compared with AC power flows generated with specialized software for flows in power systems. This allows us to demonstrate the potential of models based on DC power flows to achieve a... [more]

Regional integrated energy site layout optimization involves multi-energy coupling, multi-data processing and multi-objective decision making, among other things. It is essentially a kind of non-convex multi-objective nonlinear programming problem, which is very difficult to solve by traditional methods. This paper proposes a decentralized optimization and comprehensive decision-making planning strategy and preprocesses the data information, so as to reduce the difficulty of solving the problem and improve operational efficiency. Three objective functions, namely the number of energy stations to be built, the coverage rate and the transmission load capacity of pipeline network, are constructed, normalized by linear weighting method, and solved by the improved p-median model to obtain the optimal value of comprehensive benefits. The artificial immune algorithm was improved from the three aspects of the initial population screening mechanism, population updating and bidirectional crossov... [more]

Distribution planning refers to the act of estimating the risks of distribution systems that may arise in the future and establishing investment plans to cope with them. Forecasted loads are one of the most typical variables used to analyze the risk of the distribution system, thus the efficiency of distribution planning may vary depending on its accuracy. For these reasons, a lot of studies are also being conducted to perform load prediction by incorporating the latest methods, such as machine learning (ML). However, the unchangeable fact is that no matter what prediction method is used, the accuracy and reliability of the predicted load can vary depending on the reliability of the data used. In particular, the detection of temporary load increases, due to load transfer that can occur frequently in the distribution system are essential for securing high-quality data. Therefore, in this study, a LSTM (Long Short-Term Memory) based load transfer detection model was proposed, and the app... [more]

Energy efficiency (EE) and demand response (DR) resources provide important utility systems and ratepayer benefits. At the same time, the rapid change in the amount and type of variable renewable energy, like solar and wind, is reshaping the role and economic value of EE and DR, and will likely affect the time-dependent valuation of EE and DR measures. Utilities are increasingly interested in integrating EE and DR measures as a strategic approach to improve their collective cost-effectiveness and performance. We develop a framework to identify the EE and DR attributes, system conditions, and technological factors that are likely to drive interactions between EE and DR. We apply the framework to example measures with different technology specifics in the context of different utility system conditions. We find that EE and DR interactions are likely driven by changes in discretionary load, the addition of controls or other capabilities to shift loads, and the coincidence of savings with s... [more]

Smart grid (SG) is a next-generation grid which is responsible for changing the lifestyle of modern society. It avoids the shortcomings of traditional grids by incorporating new technologies in the existing grids. In this paper, we have presented SG in detail with its features, advantages, and architecture. The demand side management techniques used in smart grid are also presented. With the wide usage of domestic appliances in homes, the residential users need to optimize the appliance scheduling strategies. These strategies require the consumer’s flexibility and awareness. Optimization of the power demand for home appliances is a challenge faced by both utility and consumers, particularly during peak hours when the consumption of electricity is on the higher side. Therefore, utility companies have introduced various time-varying incentives and dynamic pricing schemes that provides different rates of electricity at different times depending on consumption. The residential appliance sc... [more]

Because of the stochastic nature of wind turbines, the output power management of wind power generation (WPG) is a fundamental challenge for the integration of wind energy systems into either power systems or microgrids (i.e., isolated systems consisting of local wind energy systems only) in operation and planning studies. In general, a wind energy system can refer to both one wind farm consisting of a number of wind turbines and a given number of wind farms sited at the area in question. In power systems (microgrid) planning, a WPG should be quantified for the determination of the expected power flows and the analysis of the adequacy of power generation. Concerning this operation, the WPG should be incorporated into an optimal operation decision process, as well as unit commitment and economic dispatch studies. In both cases, the probabilistic investigation of WPG leads to a multivariate uncertainty analysis problem involving correlated random variables (the output power of either win... [more]

As interest in sustainable design increases, many methods have been suggested to develop an integrated sustainable design process. However, due to the lack of a scientific procedure using parametric tools for an objective evaluation, it is difficult to move forward with integrated sustainable design. In addition, the design priority of the indoor environment is still relatively low because of the score composition of the green-building certification system. Therefore, this study aimed to develop a simulation tool and method to help apartment-complex layout planning in urban contexts by focusing on the indoor daylight environment. In particular, Korean cities are densely formed with high-rise buildings in a small area, so the Korean Building Act has complicated provisions to reduce overshadowing between buildings. To reduce unnecessary wasted time while checking these complicated regulations, a simulation was used to automatically check building offsets. Galapagos, a component of Rhino-... [more]

Sweden has committed to reducing greenhouse gas (GHG) emissions to net-zero by 2045. Around 20% of Sweden’s annual CO2 emissions arise from manufacturing, transporting, and processing of construction materials for construction and refurbishment of buildings and infrastructure. In this study, material and energy flows for building and transport infrastructure construction is outlined, together with a roadmap detailing how the flows change depending on different technical and strategical choices. By matching short-term and long-term goals with specific technology solutions, these pathways make it possible to identify key decision points and potential synergies, competing goals, and lock-in effects. The results show that it is possible to reduce CO2 emissions associated with construction of buildings and transport infrastructure by 50% to 2030 applying already available measures, and reach close to zero emissions by 2045, while indicating that strategic choices with respect to process tec... [more]

The expected growth of the number of electric vehicles can be challenging for planning and operating power systems. In this sense, distribution networks are considered the Achilles’ heel of the process of adapting current power systems for a high presence of electric vehicles. This paper aims at deciding the maximum number of three-phase high-power charging points that can be installed in a low-voltage residential distribution grid. In order to increase the number of installed charging points, a mixed-integer formulation is proposed to model the provision of decentralized voltage support by electric vehicle chargers. This formulation is afterwards integrated into a modified AC optimal power flow formulation to characterize the steady-state operation of the distribution network during a given planning horizon. The performance of the proposed formulations have been tested in a case study based on the distribution network of La Graciosa island in Spain.