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Cell lineage has been used to explain the stomatal distribution in several plant species. We have used transgenic plants carrying a 35SGUS::Ac construct that produces clonal sectors to analyze the possible role of cell lineage during the establishment of stomatal patterning in Arabidopsis leaves. The analysis of sectors ranging from two to eighteen cells supports the conclusion that most stomatal complexes derive from a single and immediate precursor cell through a stereotyped pattern of three unequal cell divisions followed by a final equal one. In addition, it shows that the successive cell divisions take place at a constant angle (approximately 60 [degrees]) with respect to the previous one. Interestingly, this angular dimension shifts from 60 [degrees] to 0 [degrees] in the last cell division that gives rise to the stoma. These sectors also reveal the development of both clockwise and counterclockwise patterns of cell divisions during stomatal development in approximately equal numbers. Our clonal analysis indicates that cell divisions involved in the development of stomatal complexes are probably the last ones contributing to epidermal growth and development. Finally, the stereotyped pattern of cell divisions that culminates in the formation of stomatal complexes indicates that cell lineage plays a very important role during stomatal pattern establishment. Key Words: Activator element; anisocytic stomatal complex; Arabidopsis; clockwise; clonal analysis; counterclockwise; epidermis; monoclonal; polyclonal; stomatal pattern.

• A MILP model is introduced to regulate peak demand. • Efficient coordination of DERs and load shifting to improve LF. • Optimal bulk scheduling of equipment used in the EN. • Mutual benefits for customers and the energy company. • The proposed model found the optimal solution to the problem. In recent decades, in urban centers, several services (such as health, food, security, connectivity, etc.) have become more dependent on electricity. With the population increase, the energy requirements of these services can lead the electricity network (EN) to a state of operational stress. In this scenario, distribution companies (DISCOs) need to develop efficient energy management strategies to avoid wasting electricity in its EN. The load factor (LF) of an EN is an indicator related to efficient electricity usage within a time horizon (e.g., day, month, year, etc.). LF improvement can be achieved through an efficient operational scheme that combines several strategies. Among them, peak load regulation in the demand profile of all the consumers, together with the operation of distributed energy resources (DERs), such as renewable energy sources (RESs) and capacitor banks (CBs), represents a promising alternative. In order to address this efficient scheme, this work proposes a mixed-integer linear programming (MILP) model that aims to increase the LF through the bulk scheduling of residential, commercial, and industrial electrical appliances during the day, considering a baseline demand and an hourly rate. At the same time, voltage fluctuations and technical losses are mitigated by the presence of DERs in the EN. Operational constraints related to the performance of EN, RESs, and CBs are considered. Uncertainties in the habitual demand profiles and solar irradiance during the day are represented through simulation algorithms. In order to evaluate the proposed model, a modified 14-node IEEE EN was used. The results corroborate the applicability of this proposed MILP model, reducing electricity waste and ensuring an efficient EN operation. [ABSTRACT FROM AUTHOR]

Highlights • Sunscald is an important postharvest disorder on apples Granny Smith. • Two prediction model with Vis-NIR reflectance were developed for sunscald on apples. • Model A predicted sunscald 45 days in advance on apples without sunburn at harvest. • Model B predicted sunscald 30 days in advance on apples with mild sunburn at harvest. Abstract Sunscald on apples (Malus domestica Borkh) is a physiological disorder that develops post-harvest in susceptible cultivars such as Granny Smith. Symptoms appear on the skin as dark brown areas over fruit sections that were sun exposed during its growth on the tree. Currently, there is no chemical control to prevent its appearance. The objective of this study was to develop a prediction model for this disorder using Vis/NIR reflectance. During 2013/14 and 2014/15 Granny Smith apples were harvest and classified according to their sun exposure in the field (shaded, sun-exposed) and sun injury symptom (mild, moderate, severe), and stored at 0–1 °C (> 90% RH) for 120 d. Every 15 d sunscald incidence was recorded, and spectral fingerprint taken using an optical portable spectrometer Vis/NIR (350–1100 nm). Multivariate analysis was performed using partial least squares discriminant analysis (PLS-R) and intervals PLS-DA (iPLS-DA) in order to develop calibration models and applied them to a prediction set. Two calibration models were developed. The first one (A; R2 CV = 0.41) for fruit at harvest with no sun damage symptoms (shaded and sun-exposed, n = 161), which was able to predict the appearance of sunscald 45 d in advance. The spectral ranges responsible for discrimination (iPLS-DA) of fruit with (Class 1) and without (Class 0) sunscald postharvest were: 500–525 nm, 550–575 nm, 650–675 nm, 775–800 nm, 850–875 nm, and 925–950 nm. The second model (B; R2 CV = 0.59) was developed for mild sun damaged fruit (n = 100), which predicted sunscald 30 d in advance. In this case, the spectral ranges responsible of discrimination were 500–525 nm, 650–675 nm, and 775–800 nm, related to pigments and structural properties of the tissue. The RMSEP for the independent fruit set validation models (n = 69) were 3-4-times higher than that of the calibration models, probably because biological variability. A greater number of independent fruit sets will be required to increase models' robustness and prediction capability for the industry adoption. [ABSTRACT FROM AUTHOR]

This paper develops an energy policy measure for renewable sources in Colombia, in particular wind generation. The proposal is done at the decentralized level, in isolated areas of the country, where electricity coverage is below 12% and wind speed is suitable for power generation. The goal of this policy is focused on increasing electricity coverage in those remote areas of the country that have high winds in order to develop clean generation investments that can represent a benefit for low-income users. Thus, a mechanism for financing these kinds of investments is proposed, involving the private sector and using the mechanism known as Public Private Partnerships – PPPs. PPPs are mechanisms used by the public sector to establish a contract with the private sector. The private sector provides capital and ability to develop projects, while the public sector holds the responsibility in service delivery. To model the relationship between public sector and private investors, a bilevel programming method for efficient resource allocation, combining an auction mechanism and moral hazard, is presented. A case study is shown in the Colombian context. [ABSTRACT FROM AUTHOR]

A method based on Empirical Martingale Simulation (EMS) is presented to evaluate investments in wind energy. Risk-neutral prices are calculated, where electricity market prices are modeled using an ARIMA–GARCH method which shows conditional heteroskedasticity. The values of the put options are calculated a week ahead and it is observed that wind producers that invest in the options market can hedge against price risk and can also maximize their benefits. The use of Monte Carlo simulation with the EMS method in periods of high volatility is especially useful for investors facing price volatilities in order to improve their returns. The model is applied to the Colombian electricity market. [ABSTRACT FROM AUTHOR]

Abstract: Listeria monocytogenes is an important agent of foodborne disease, showing low prevalence but high mortality. There is evidence that vegetables are important vehicles of transmission, especially those minimally processed (fresh, fresh-cut vegetables under modified atmosphere packaging (MAP) or frozen) and directly exposed to consumers. The aim of this work was to detect and enumerate viable L. monocytogenes cells by culture and molecular methods from vegetable foods. A total of 191 vegetable samples (fresh, frozen and fresh-cut under modified atmosphere packaging) were studied. L. monocytogenes was detected and identified by selective plating, PCR and DVC-FISH. An isolation rate of 4.19% was obtained by culture, with a higher incidence in frozen vegetables. Six isolates belonged to serotype 1/2a and 2 to 4b. Counts were below <100 cfu/g for the eight positive samples, according to the food safety criteria established for the RTE by Commission Regulation EC No 1441/2007. Multiplex PCR method yielded a greater number of positive samples (10.47%). DVC-FISH technique determined that viable cells of L. monocytogenes were present in 32.98% of the samples, containing up to 4.97 log10 viable cells/g. [Copyright &y& Elsevier]

Abstract: This paper presents an approach based on a specialized genetic algorithm (GA) to determine the location and contract pricing of dispatchable distributed generation (DG) units in distribution systems. The proposed approach is based on a nonlinear bilevel programming framework that involves the interests of two different agents: the DG owner who procures the maximization of the profits obtained from the energy sales, and the Distribution Company (DisCo), which procures the minimization of the payments incurred in attending the forecasted demand. To meet the forecasted demand the DisCo can purchase energy either from the wholesale energy market or from the DG units within its network. The proposed GA determines both the location and contract pricing of the DG units that would render maximum profits to the DG owner, subject to the minimization of payments procured by the DisCo. To show the effectiveness of the proposed approach, several tests were carried out on a modified IEEE 34-bus and 85-bus distribution networks. [Copyright &y& Elsevier]

Abstract: Background: Although genetic influences on bipolar I disorder are well established, localization of genes that predispose to the illness has been difficult. Some genes predisposing to bipolar I disorder may be transmitted without expression of the categorical clinical phenotype. One strategy to overcome this obstacle is the use of quantitative endophenotypes, as has been done for other medical disorders. Methods: We analyzed 30 bipolar I extended families (300 subjects, average family size 10.34 members, range: 2–31) and 20 unrelated healthy controls from a Costa Rican sample. Heritability and genetic correlation of the state and trait scale from the Anxiety State and Trait Inventory was computed by using the general linear model (SOLAR package software). We also assessed variation of both scores among groups (patients, relatives and controls) and tested independence of affection status. Results: Heritability for state is 0.45 (SE=0.11, p =0.0000001) and for trait is 0.89 (SE=0.06, p =6.22e−29). Genetic correlation for state and trait is 0.29, (SE=0.12, p =0.038–3.19e−8). Bipolar I patients showed the highest trait score (F =12.17 [5,24], p =0.002), (bipolar I patients>relatives with other pathologies, >healthy relatives>unrelated healthy controls) with normal distribution in healthy individuals and no difference regarding depression and mania current status, (F =0.230, df=1, p =0.632 and F =1.401, df=1, p =0.238, respectively), contrary to the state score. Limitations: Confounding factors such as comorbid disorders could affect the interaction of subclinical anxiety with mania. Due to our limited budget we were not able to re-evaluate the subjects and conduct a test retest to assess the STAI reliability and mood state independence of anxiety traits over different times. Further research is needed to evaluate if anxiety traits are specially related to bipolar I disorder in comparison with other traits such as anger, attention or response inhibition deficit, pathological impulsivity or low self-directedness. Conclusions: Anxiety state and trait are heritable and share some genetic factors but only trait showed normal distribution in healthy subjects, mood current status independence and significant liability for bipolar I disorder. A stair-step distribution of trait anxiety scores in the family members and controls based on their genetic proximity to affected individuals and diagnostic status suggests that trait anxiety could be an endophenotype in these bipolar I families. [Copyright &y& Elsevier]

• Radial Flesh Browning (FB) development was associated with sphingolipids metabolism. • Glucocerebrosides and ceramides were found in apples at risk of FB development. • Ceramides may have a key role in early chilling stress events leading to radial FB. • Ceramides may be used for forensic identification of radial FB. Flesh browning (FB) of 'Cripps Pink' apples can cause significant economic losses to apple producers worldwide. FB symptoms and etiology can vary for this cultivar. Specific metabolic fingerprint has the potential for identification and understanding of different disorders leading FB postharvest. Flesh tissue from fruit collected starting 150 days after full bloom, through harvest, then, until 7 months into cold storage were sampled for non-targeted metabolic analyses using gas and liquid chromatography coupled with mass spectrometry. Radial FB development was positively associated with sphingolipids metabolism. Elevated levels of multiple glucocerebrosides and ceramides were found in apples at risk for developing the disorder and with symptoms, while the concentrations of a different group of inter-related metabolites tentatively as sphingolipids increased in healthy tissue during storage. Based on previous reports in other plant and animal species, elevated sphingolipid concentrations may be associated with membrane lipid catabolism, cell apoptosis, and fruit senescence involved in FB symptom development. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an analysis and discussion, based on cooperative game theory, for the allocation of the cost of losses to generators and demands in transmission systems. We construct a cooperative game theory model in which the players are represented by equivalent bilateral exchanges and we search for a unique loss allocation solution, the Core. Other solution concepts, such as the Shapley Value, the Bilateral Shapley Value and the Kernel are also explored. Our main objective is to illustrate why is not possible to find an optimal solution for allocating the cost of losses to the users of a network. Results and relevant conclusions are presented for a 4-bus system and a 14-bus system. [Copyright &y& Elsevier]

Abstract: Electricity market simulators have become a useful tool to train engineers in the power industry. With the maturing of electricity markets throughout the world, there is a need for sophisticated software tools that can replicate the actual behavior of power markets. In most of these markets, power producers/consumers submit production/demand bids and the Market Operator clears the market producing a single price per hour. What makes markets different from each other are the bidding rules and the clearing algorithms to balance the market. This paper presents a realistic simulator of the day-ahead electricity market of mainland Spain. All the rules that govern this market are modeled. This simulator can be used either to train employees by power companies or to teach electricity markets courses in universities. To illustrate the tool, several realistic case studies are presented and discussed. [Copyright &y& Elsevier]

Abstract: This paper presents the complete design of a photovoltaic installation that may be either used for internal electric consumption or for sale using the premium subsidy awarded by the Spanish Government. Electric optimization strategies are detailed in the project, as well as the sizing of the photovoltaic installation and economic and financial issues related to it. The project optimizes the electricity demand, improving reactive power and studying the convenience of hourly discrimination fees in addition to the design of the photovoltaic installation. A specific computer application for the automated calculation of all relevant parameters of the installation—physical, electrical, economical as well as ecological—has been developed to make the process of calculating photovoltaic installations easier and to reduce the design development time. Moreover, the budget of the photovoltaic installation is included, as well as its corresponding financial ratios and payback periods. Finally, the conclusions reached in the technical and economic design of the installation are shown. [Copyright &y& Elsevier]

• A MILP model to ensure efficient usage of electricity is introduced. • Focusing on mitigating peak demand in low tariff periods. • Improving the load factor of a group of consumers. • Ensuring mutual benefits to customers and the energy company. • The CPLEX solver allowed to guarantee the optimal solution of the problem. Demand response (DR) programs based on pricing options allow residential customers to achieve a financial reduction in their energy bill due to changes in their consumption patterns, especially during peak periods. However, when a large number of consumers adopt this energy management program, the demand shifted to periods with low energy prices can generate new demand peaks. As a result, the quality of the power supply service may be compromised. To address this concern, this paper proposes a mixed-integer linear programming (MILP) model that aims to improve the load factor (LF) related to the demand profile of customers. To achieve this goal, an intelligent scheduling strategy for household appliances that considers flexibility in customer comfort, here called customer hourly preferences, is developed. Based on these preferences, the strategy seeks the efficient daily usage of smart appliances, mainly those with higher average power, to avoid its coincident consumption in periods with lower energy rates, thus mitigating the appearance of new peaks. In the proposed model, the operating expenses of both customers and the electricity company (ECO) are minimized. A set of technical and operational constraints such as the average power, number of times utilized, and average time of usage of home appliances, as well as the charging rate, average time for charging, and initial state-of-charge (SoC) of the plug-in electric vehicle (PEV) battery, are considered. Uncertainties related to the periods of the day when a given appliance (including PEV) is turned-on for consumption are modeled using a Monte Carlo Method (MCM). The MILP model is solved using a commercial solver CPLEX that makes use of classical optimization techniques to ensure the optimal solution to this problem. The performance of the MILP model was tested through two case studies. Case study 1 considers a group of consumers with the same income, while case study 2 triples the number of consumers in the previous case considering different incomes. The results show the importance of the proposed tool for analyzing and evaluating prospective scenarios that guarantee the efficient usage of electric energy with the lowest financial expense for both consumers and the ECO. [ABSTRACT FROM AUTHOR]

Implementing local distribution markets has been pointed out to simultaneously deal with proactive customer bids and coordinate their power dispatch. However, the impacts of such coordination in the transmission system are usually disregarded. Additionally, proposals that aim to coordinate the operation of distribution and transmission systems usually regard the distribution system operator as a strategic price-maker in the wholesale market, while other wholesale market agents with similar market power are not. In this paper, we propose a trilevel programming framework in which the wholesale market is modeled in the upper level, the wholesale market agents, namely generation and distribution companies, are characterized in the middle level, and the distribution-connected proactive customers are considered in the lower level. The resulting formulation allows the incorporation of multiple agents in any of the markets without the need to change the solving technique. Furthermore, the energy market clearing processes regard every agent participating in the same market at the same hierarchy. A formulation to enforce uniform energy prices is proposed to overcome the inaccessibility to marginal prices under the proposed framework. Numerical results are presented for a power system composed of distribution and transmission networks with multiple independent agents to validate the proposed approach. • Simultaneously representing wholesale and local distribution market clearing. • Modeling every market agent as a price-maker. • Regarding every market agent in the same hierarchy. • Problem reformulation as an MPEC rather than an EPEC. [ABSTRACT FROM AUTHOR]

The electricity market liberalization propelled by the increased operational flexibility observed in distribution systems urges the development of mathematical models for competitive electricity markets accounting for the participation of distribution systems operators. Most existing approaches model the interaction between the agents and the market as a bilevel programming problem, wherein the market is characterized at the lower level. Hence, the upper-level agent's objective function is favored over minimizing the costs of supplying the system's demand. In this paper, a new bilevel formulation is proposed. In the upper level, the market operator determines the optimal energy price that ensures the demand supply and maximizes the overall social welfare. In the lower level, the generation companies and distribution systems operators determine the amount of energy injected/extracted into/from the transmission system to maximize profits/minimize operational costs. A new pricing method is proposed in this paper as an alternative to the transmission system's dual variables, which are inaccessible under the proposed framework. The resulting nonlinear mixed-integer bilevel programming formulation is transformed into an equivalent single-level mixed-integer linear program. Numerical results illustrate the proposed approach's effectiveness in maintaining the minimum operational costs while distributing the payoff amongst the agents. • Bilevel formulation for wholesale markets with non-strategic price-maker agents. • New market framework with a clearing process that minimizes the total costs. • Development of a new pricing mechanism for electricity markets. • Energy storage systems and demand response minimizes the clearing prices. [ABSTRACT FROM AUTHOR]

Terpenylated phenols possess interesting biological activities. These properties vary mainly according to the type of terpene associated and the degree of oxidation of the molecule. The search for new active molecules for application in different areas of knowledge includes the structural modification of these through ecological methodologies, such as biotransformation. The aims of this study were the biotransformation of geranylated- and acetylated-phloroglucinol by the fungus Gibberella fujikuroi and the evaluation of the antifungal activity of the derivatives. Five major derivatives were identified after biotransformation, highlighting the formation of specific monoacetylated products. In vitro antifungal activity assays against the phytopathogenic fungus Botrytis cinerea indicated that deacetylated derivatives possess higher activity compared to the precursor molecule. In other biotransformation reactions, a relationship between the release of the alkyl chain from the aromatic ring with a decrease of the antifungal activity, was observed. The in vivo tests in infected tomato plants with B. cinerea confirmed the antifungal activity of the derivatives observed in in vitro experiments. [ABSTRACT FROM AUTHOR]

A fundamental challenge in power system planning is how to handle the interactions of participants’ behaviors in deregulated markets. This is important due to the high cost involved in their decisions. Proactive or anticipative transmission expansion planning models have been proposed by some authors to jointly model the interactions among deregulated electricity market participants making market-driven investment decisions. Several works have shown that a Transmission Network Planner can increase social welfare by anticipating line expansion planning to generation expansion equilibrium and market outcomes. However, proactive transmission expansion decisions may lead to suboptimal solutions when the generation expansion equilibrium problem have multiple solutions (i.e., leading to higher total costs and lower social welfare). We propose a methodology to study the potential impacts of proactive expansion planning on generation expansion decisions. The resulting formulation is stated as a mathematical program subject to an equilibrium problem with equilibrium constraints (EPEC). To deal with this problem, we also propose an approach to derive tractable EPEC solutions with global optimality guaranteed based on a column-and-row generation algorithm. Our numerical results shows that a proactive investment plan can lead to higher total cost than not investing at all because of the existence of multiple market-driven generation expansion equilibria. We show that the proposed algorithm significantly reduce the time of computation up to two orders of magnitude with respect to existing methodologies. [ABSTRACT FROM AUTHOR]

Sun-injury (=sunburn) on apples, as well as in other species, is a physiological disorder that develops in the field when high irradiance combines with elevated temperature during the growing season. This causes photooxidative and heat stress that finally leads to symptoms in sun-exposed fruit tissue. The objective of this study was to assess metabolite profiles changes, as a consequence of these abiotic stresses, using VIS/NIR reflectance in Granny Smith apples. Individual fruit was tagged and weekly monitored from 60 until 151 DAFB. Vis/NIR reflectance measurements (350–1100 nm) were taken on the fruit surface on apples unexposed to direct sunlight (NE, class 0), exposed to direct sunlight (EX, class 1) and those that developed sunburn in the categories of Mild (class 2), Moderate (class 3), and Severe (class 4). Spectral curves were analyzed using discriminant partial least square models (PLS-DA) and interval PLS discriminant analysis (iPLS-DA). PLS-DA analysis was able to separate fruit with and without visible sun injury with a classification error of 22.5% and 35.7% for class 3 and 2, respectively. Class 3 and 4 observed at 129 DAFB was predicted through reflectance spectra captured 87 DAFB by PLS-DA, with 550–570 nm, 715–750 nm, and 810–850 nm spectral intervals responsible ranges for discrimination. Such changes in Vis/NIR reflectance among classes would be related to changes in pigments (carotenoids and flavonoids) and apple tissue structural properties. These findings indicate that the method offers potential for nondestructive prediction of sunburn occurrence on apples in the orchard, that could lead to development of an automated system to forecast sunburn occurrence. [ABSTRACT FROM AUTHOR]

Energy hubs play an important role in implementing multi-carrier energy systems. More studies are required in both their modeling and operating aspects. In this regard, this paper attempts to develop medium-term management of an energy hub in restructured power systems. A model is presented to manage an energy hub which has electrical energy and natural gas as inputs and electrical and heat energy as outputs. Electricity is procured in various ways, either purchasing it from a pool-based market and bilateral contracts, or producing it from a Combined Heat and Power (CHP) unit, a diesel generator unit and Wind Turbine Generators (WTGs). Pool prices and wind turbine production are subject to uncertainty, which makes energy management a complex puzzle. Heat demand is also procured by a furnace and a CHP unit. Energy hub managers should make decisions whether to purchase electricity from the electricity market and gas from the gas network or to produce electricity using a set of generators to meet the electrical and heat demands in the presence of uncertainties. The energy management objective is to minimize the total cost subject to several technical constraints using stochastic programming. Conditional Value at Risk ( CVaR ), a well-known risk measure, is used to reduce the unfavorable risk of costs. In doing so, the proposed model is illustrated using a sample test case with actual prices, load and wind speed data. The results show that the minimum cost is obtained by the best decisions involving the electricity market and purchasing natural gas for gas facilities. Considering risk also increases the total expected cost and decreases the CVaR . [ABSTRACT FROM AUTHOR]

This paper presents a new approach for long-term reactive power investment planning and operation using a multiperiod mixed-integer stochastic convex model, where load uncertainty is also included. The risk of not meeting the load with a certain level of confidence due to a reactive power deficit is represented by chance constraints. Tap settings of under-load tap-changing transformers are modeled as a set of mixed-integer linear equations. Existing and new discrete and continuous reactive power sources are modeled. These type of problem is challenging and have never been studied before. The proposed model is applied to the CIGRE-32 electric power system. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic framework for offering and bidding strategies of a hybrid power generation system (HPGS) with a wind farm and two types of energy storage facilities, i.e., compressed air energy storage (CAES) and battery energy storage (BES) systems. The model considers the participation of the HPGS in consecutive electricity markets, i.e., day-ahead (DA) and intraday markets. To better address the proposed trading strategy problem, the BES degradation cost is also incorporated into the model. Furthermore, a mechanism based on energy procurement from demand response resources (DRRs) in the intraday demand response exchange (IDREX) market for the HPGS is also established to offset unexpected energy imbalances effectively. The suggested offering and bidding strategy is formulated as a three-stage stochastic programming problem incorporating a risk-alleviating index, namely, the conditional value-at-risk (CVaR). Results from several simulations indicate considerable profit gain and risk reduction achieved by the suggested offering and bidding framework. • Operating model of a wind-compressed air energy storage-battery system is developed. • The impact of the intraday demand response exchange market is thoroughly analyzed. • Role of battery degradation cost in joint and disjoint operating models is studied. • A three-stage scenario-based architecture is suggested to cope with the uncertainties. • Joint operating strategy ensures economic betterment compared to the disjoint one. [ABSTRACT FROM AUTHOR]

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%. [Display omitted] • Detailed features of electric vehicle operation and parcel delivery are considered. • The proposed model links delivery routing and aggregated energy management. • An electric vehicle demand aggregator guarantees the synergy between systems. • The model stages are: delivery allocation, delivery routing and energy management. • The incentives allow the electric vehicle aggregator to achieve cost savings of 8.5%. [ABSTRACT FROM AUTHOR]

In recent decades, the ever-increasing demand for electricity in large cities is leading the electricity distribution network (EDN) to an operational state of fatigue, especially at peak load times. In this context, load congestion, technical losses, and voltage deviations across the EDN can contribute to the high waste of electricity as well as to the worsening of the supply service. To face this problem, this paper proposes a mixed integer linear programming (MILP) model that aims to reshape the demand profile (i.e., load reduction in peak periods without load rebound in the remaining periods of the day) of total consumers while the voltage deviations and technical losses are minimized. In the proposed model, the demand profile reshaping is carried out through direct (DC) and indirect (IC) bulk control of residential, commercial, and industrial loads, while the load factor (LF), i.e., indicator related to the efficient use of electricity, is improved. Due to the scheduling of these loads at certain times of the day, the occurrence of voltage deviations and technical losses can be mitigated by the efficient control of capacitor banks (CBs). To corroborate the applicability of the proposed MILP model, the 33-node IEEE test system was used. The results show the technical and economic gains for the distribution company (DISCO) and the total number of consumers. • A MILP model is proposed to reduce electricity waste in EDN. • DC and IC of bulk loads to reduce peak demand without occurrence of load rebound. • Control of CBs to mitigate EDN under and over voltages due to load variation. • Improvement of LF related to the EDN demand profile to alleviate operational stress. [ABSTRACT FROM AUTHOR]

Abstract: This work is devoted to study and discuss the main methods to solve the network cost allocation problem both for generators and demands. From the presented, compared and discussed methods, the first one is based on power injections, the second deals with proportional sharing factors, the third is based upon Equivalent Bilateral Exchanges, the fourth analyzes the power flow sensitivity in relation to the power injected, and the last one is based on network matrix. All the methods are initially illustrated using a 4-bus system. In addition, the IEEE 24-bus RTS system is presented for further comparisons and analysis. Appropriate conclusions are finally drawn. [Copyright &y& Elsevier]