Cost-optimal Way Research Articles

Roadmap towards clean and low-carbon heating to 2060: The case of northern urban region in China

Roadmap towards clean and low-carbon heating to 2060: The case of northern urban region in China

Achieving −55% GHG emissions in 2030 in Wallonia, Belgium: Insights from the TIMES-Wal energy system model

The Walloon Region has undertaken the ambitious engagement to reduce its greenhouse gases (GHG) emissions up to −55% in 2030. In this context, a regional model of the energy system is a useful tool to give insights to policy makers. We address the lack of an existing integrated tool by developing a technology-rich, bottom-up model for the region. The goal of this paper is twofold: we present the model and its functioning and then we analyse a cost-optimal way to reach the −55% regional target. Firstly, we describe the methodology, discussing how we build the sectors of our model and how the optimisation works. Secondly, we run the model with a constraint on GHG emissions to assess the impact of the mitigation target. We show that the total system cost of such an ambitious mitigation scenario is only ∼0.5% higher than the cost of an unconstrained reference scenario and that emissions reduction must start as soon as possible to stay on the cost-effective trajectory. Concerning technologies, windmills, photovoltaic (PV) panels and building renovations are cost-optimal solutions even with high discount rates.

BitFund: A blockchain-based crowd funding platform for future smart and connected nation

Abstract The rapid growth in information technology and related talent has let to a competition among the investors to look for the best available talent. Additionally, the diverse range of directions in technology, gives a lot of options to the applicants to choose from. In such a scenario, it is imperative to link the most appropriate investors and developers in a secure and cost optimal way. Blockchain technology helps in creating a decentralized network of users where the transactions are recorded in an open distributed ledger. These features of blockchain enable a transparent and cost-effective platform for different applications. Based on the need to an effective crowdfunding platform for developing smart nation and the inherent features of blockchain technology, we propose a global crowdfunding platform called BitFund. Investors and developers can act as different nodes of the network. The investors can request a specific project and they can give their initial bid value in terms of time, cost and maintenance required. Different developers can bid with different values of the same parameters to get the project ownership. A smart contract is deployed between the investors and the developers to reach an optimal solution for the investors. Multiple iterations of bidding are carried out between the developers until the optimal solution or equilibrium is reached. The experimental results show that the proposed model yields better results as compared to other generic algorithms for crowdfunding.

A Distributed Framework for Energy Trading Between UAVs and Charging Stations for Critical Applications

Use of Unmanned Aerial Vehicles (UAVs) is rapidly increasing in various domains such as disaster management, delivery of goods, surveillance, military, etc. Significant issues in the expansion of UAV-based applications are the security of (IoT to UAV) communication, and the limited flight time of the UAVs and IoT devices considering the limited battery power. Standalone UAVs are not capable of accomplishing several tasks, and therefore swarm of UAVs is being explored. Security issues in the swarm of UAVs do not allow the applications to leverage the full benefits that one can offer. Several recent studies have proposed the use of a distributed network of UAVs to upgrade the level of security in the swarm of UAVs. In this paper, a framework for secure and reliable energy trading among UAVs and charging stations is presented. Advanced blockchain, based on the tangle data structure is used to create a distributed network of UAVs and charging stations. The proposed model allows the UAVs to buy energy from the charging station in exchange for tokens. If the UAV does not have sufficient tokens to buy the energy, then the model allows the UAV to borrow tokens from the charging station. The borrowed tokens can be repaid back to the charging station with interest or late fees. A game-theoretic model is used for deciding the buying strategy of energy for UAVs. Numerical analysis shows that the proposed model helps in providing increased utility for the swarm of UAVs and charging stations in a secure and cost-optimal way as compared to the conventional schemes. The results can eventually be applied to IoT devices that constantly need energy to perform under ideal conditions.

CONCEPTS «LOGISTIC SERVICE» AND «LOGISTIC SERVICING»

In order to fully meet the needs and demands of consumers, logistics services or logistics servicing are used and developed. However, in modern literature and practice, there is no single definition of these concepts, their identification or delineation occurs. This complicates the work on the formation of the logistics service system (logistics servicing), the development of recommendations for the quality of logistics services, etc. In this work a critical analysis of scientific and practical approaches to the definition of the concepts of «logistic service» and «logistic servicing» has been carried out, the necessity of their delineation has been proved. The term «logistic service» is proposed to be used in the case of activity directed at a particular consumer, his needs, requests, etc.; in the opposite, when it comes to the whole potential and the real possibility of organization (service, etc.) to provide logistics services - the term «logistics servicing» is relevant. In this case, logistic servicing is defined as the organized activities of personnel in a particular material environment, associated with the supply and provision of logistics services that meet the specific needs and requirements of consumers. Logistic service is a complex of logistic services offered and provided to customers by a service or other organization, in order to maximally meet their needs, in the most cost-optimal way. As a rule, the concept of «logistics service» should be used to outline the scope of the provision of logistics services to an external customer on a commercial basis. Prospects for further research are the establishment of the classification of logistics services, the development of approaches to the formation of logistics services, organization of logistics servicing for consumers.

The influence of continued reductions in renewable energy cost on the European electricity system

Abstract In the recent years, variable renewable energy (VRE) technologies – most importantly solar photovoltaics and wind power – have undergone a remarkable transformation from niche technologies to increasingly competitive energy suppliers. As the potential for VRE is distributed unevenly across Europe, a Europe-wide cost optimal expansion of VRE will lead to different national and regional expansion rates of VRE. To facilitate such a Europe-wide cost optimal expansion of VRE a fair cost distribution among all European countries is needed. Therefore, we analyse and discuss how expected future decreases in investment costs for selected VRE (photovoltaics, concentrated solar power, wind onshore, and wind offshore) will affect the pan-European and national electricity systems. This is done by comparing three cost scenarios with a reference case, calculated using a European electricity system model. Our results show that the assumed cost reductions lead to an especially pronounced increase of PV distributed unevenly across Europe. In addition, higher shares of VRE show the effect of shifting electricity exchange patterns throughout Europe which also reduce cost benefits for economies of electricity exporting countries. Hence, there might be a lack of agreement in Europe about where and how to expand and integrate VRE if costs of expanding and integrating VRE are not distributed in a fair way between the European countries. In addition, these possible barriers of expanding VRE Europe-wide in a cost-optimal way might hinder the exploitation of cost synergies or even slow down VRE expansion on a European scale.

Control strategies for decreasing energy costs and increasing self-consumption in nearly zero-energy buildings

Abstract This article presents an extensive evaluation which analyzes the contribution of several control methods to decreasing the energy consumption associated with heating and cooling of buildings and the contribution of the local energy production to minimizing energy costs in nearly zero-energy buildings (nZEB). A set of three heating and cooling control methods is mixed with a set of cost-based optimum allocation of on-site energy production using linear programming, variable indoor temperature setpoint for following the production curve and a battery storage system. When designing nZEBs, the aim is not only to reduce the energy consumption, but also to reduce the lifecycle costs, and a big part of them is associated with energy needed for heating and cooling. nZEBs are supposed to have local renewable energy generation, which must be consumed in a cost optimal way for keeping the total lifecycle costs low. This research helps nZEB design collectives to assess the opportunity of implementing controls as the ones described in this study and policy makers to extrapolate the results and estimate how the energy consumption reduction targets can be reached by implementing such technologies. The analysis of the control strategies is made based on real buildings located in Bucharest, Romania.

A quality assuring, cost optimal multi-armed bandit mechanism for expertsourcing

There are numerous situations when a service requester wishes to expertsource a series of identical but non-trivial tasks from a pool of experts so as to achieve an assured accuracy level for each task, in a cost optimal way. The experts available are typically heterogeneous with unknown but fixed qualities and different service costs. The service costs are usually private to the experts and the experts could be strategic about their costs. The problem is to select for each task an optimal subset of experts so that the outcome obtained after aggregating the opinions from the selected experts guarantees a target level of accuracy. The problem is a challenging one even in a non-strategic setting since the accuracy of an aggregated outcome depends on unknown qualities. We develop a novel multi-armed bandit (MAB) mechanism for solving this problem. First, we propose a framework, Assured Accuracy Bandit (AAB) framework, which leads to a MAB algorithm, Constrained Confidence Bound for Non-Strategic Setting (CCB-NS) . We derive an upper bound on the number of time steps this algorithm chooses a sub-optimal set, which depends on the target accuracy and true qualities. A more challenging situation arises when the requester not only has to learn the qualities of the experts but has to elicit their true service costs as well. We modify the CCB-NS algorithm to obtain an adaptive exploration separated algorithm Constrained Confidence Bound for Strategic Setting (CCB-S) . The CCB-S algorithm produces an ex-post monotone allocation rule that can then be transformed into an ex-post incentive compatible and ex-post individually rational mechanism. This mechanism learns the qualities of the experts and guarantees a given target accuracy level in a cost optimal way. We also provide a lower bound on the number of times any algorithm must select a sub-optimal set and we see that the lower bound matches our upper bound up to a constant factor. We provide insights on a practical implementation of this framework through an illustrative example and demonstrate the efficacy of our algorithms through simulations.

Strategies to improve the voltage quality in active low‐voltage distribution networks using DSO's assets

This study addresses the problem of voltage variations in active low-voltage distribution networks caused by distributed photovoltaic (PV) generation. Three strategies based on model predictive control (MPC) are introduced to flatten the voltage profile in a cost-optimal way. The compared strategies are the business as usual approach that manipulates a controllable on-load tap changer at the primary substation, the problematic feeder control strategy (CS) that adds an additional degree of freedom by controlling the critical secondary substations (SSs), and finally the compensation strategy, which controls the primary substation and compensates the non-critical SSs. A sensitivity analysis on the CSs has been conducted comparing the voltage variation reduction and the asset utilization with regard to the accuracy of the prediction models and the forecasted disturbance data. The results show that better (and more costly) characterisation of these parameters only provide a marginal improvement in the reduction of the voltage variations due to the restriction caused by the heavy tap change penalisation. Moreover, the tested case-study shows that the problematic feeder CS outperforms the compensation strategy in terms of larger voltage variation reduction for similar asset utilisation.

Determining the cost optimum among a discrete set of building technologies to satisfy stringent energy targets

AbstractThis paper presents the development of an optimization methodology for selecting the lowest monetary cost combinations of building technologies to meet set operational energy reduction targets. The new optimization algorithm introduced in this paper departs from the notion that optimal design choices over a large set of design parameters and properties can be driven by energy targets. We assume that design parameters are determined by many concurrent considerations fighting over the attention span of the design team. Our approach starts from a design outcome and asks the question, which set of discrete technologies are the right mix to reach an energy target in the cost optimal way? Such an approach has to face the challenge that the properties of market-available building technologies have a discrete nature that makes their optimal selection a combinatorial problem. The optimization algorithm searches the discrete combinatoric space by maximizing the following objective function: calculated energy savings divided by premium cost, where cost is defined as the additional cost over a baseline solution. The algorithm is codified into a custom MATLAB script and when compared to prescriptive methodologies is shown to be more cost effective and generically applicable given a palette of building technology alternatives and their corresponding cost data.

Optimizing inspection intervals—Reliability and availability in terms of a cost model: A case study on railway carriers

This paper states the problem that railway carriers have with inspections and maintenance in its most cost optimal way. Often there is external pressure to improve reliability and availability and to reduce costs significantly. To overcome this problem this paper suggests a model that tries to find the optimal inspection interval. Not all maintenance companies have their inspection intervals that match with the actual reliability of a system anymore and the inspection intervals are not necessarily at a cost optimum. This research retrieves the actual failure and repair data and combines this together with the availability of a system to find the optimum inspection interval in terms of costs. The application of the optimization approach to a railway carrier maintenance company in the Netherlands is also presented.

Implications of different climate protection regimes for the EU-27 and its member states through 2050

It is a broadly accepted fact that a clear reduction of global GHG emissions is required to limit the increase of global warming to a tolerable level. A key issue in this context is the optimal breakdown of reduction targets among different world regions or even countries. Using the European Commission-funded PLANETS project, cost-optimal global burden sharing to reach global GHG reduction targets was analysed, and an optimal allocation of GHG reductions was identified, relative to the global target, to the commitments of different world regions and the trade possibilities for emission certificates. Specifically, it is evaluated how Europe can contribute in a cost-optimal way to keeping the global concentration of GHGs in the atmosphere below 530 parts per million equivalent (ppme) or below a stricter global reduction target of 500 ppme. Based on the energy system model TIMES PanEU, the potentials for emissions reduction in the different energy sectors and EU Member States and the role of key technologies are analysed. The most cost-effective potentials for GHG reductions in Europe are in the conversion/production, residential and industrial sectors. Substantial reductions in the transport sector occur only under very stringent reduction targets. Achieving ambitious reduction targets requires considerable contributions from all EU Member States until 2050.

Model predictive control technologies for efficient and flexible power consumption in refrigeration systems

Considerable amounts of energy are consumed in supermarket refrigeration systems worldwide. Due to the thermal capacity of refrigerated goods and the rather simplistic control most commonly applied, there is a potential for distributing the system load over time in a more cost-optimal way. In this paper we describe a novel economic-optimizing Model Predictive Control (MPC) scheme that reduces operating costs by utilizing the thermal storage capabilities. A nonlinear optimization tool to handle a non-convex cost function is utilized for simulations with validated scenarios. In this way we explicitly address advantages from daily variations in outdoor temperature and electricity prices. Secondly, we formulate a new cost function that enables the refrigeration system to contribute with ancillary services to the balancing power market. This involvement can be economically beneficial for the system itself, while crucial services can be delivered to a future flexible and intelligent power grid (Smart Grid). Furthermore, we discuss a novel incorporation of probabilistic constraints and Second Order Cone Programming (SOCP) with economic MPC. A Finite Impulse Response (FIR) formulation of the system models allows us to describe and handle model as well as prediction uncertainties in this framework. This means we can demonstrate means for robustifying the performance of the controller.

A method for the hierarchical planning of the structure, dimension and material requirements of manufacturing systems

Growing competition creates pressure among companies to run their production in a cost-optimal way and yet adapt immediately to changes in their environmental conditions. Decisions must always consider the integration of the companies in globalised markets on both the demand and supply side. This paper deals with the optimal planning of decisions concerning the structure and dimension of production facilities. The methods used for material requirements planning are selected and configured according to these decisions. These decision problems are represented in the form of four hierarchically layered partial models, each of them realised and iteratively solved by a mathematical optimisation model. The hierarchical planning method is evaluated using a practical case example based on a valve production system of a manufacturing company.

Supply Chain Optimization in Pulp Distribution Using a Rolling Horizon Solution Approach

In this paper we consider a combined supply chain and ship routing problem for a large pulp producer in Scandinavia. The problem concerns the distribution of pulp to customers, with route scheduling of ships as a central part of modeling. It is an operative planning problem with daily ship routing decisions over a 40 days period. The pulp supply is determined by fixed production plans, and the transport flows and storages are modeled with the requirement to satisfy the demand in a cost-optimal way. We develop a mixed integer programming model with binary variables for route usage of a vessel. The problem is solved with a heuristic solution method, based on a rolling time horizon and a standard branch and bound algorithm. We apply the heuristic on problem instances with real world data, and compare results from reduced problem instances with the results from an exact branch and bound search. The computational experiments indicate that real world problems are solvable with the solution method and that it in many cases can be very efficient.