Search

The aggressive goal of having a 100% renewable energy system requires preparing an appropriate infrastructure for deploying as much distributed generation (DG) as possible into the electricity network. This article aims at proposing a new framework to maximize the hosting capacity (HC) of DGs in a distribution network. This operation-planning framework provides a suitable and interactive environment for the electricity market, distribution company (DISCO), electric vehicle (EV) aggregator, and rival DISCOs to increase the HC. The problem is modeled via a bilevel conditional-value-at-risk-constrained stochastic programming approach. The DISCO, in the upper level (UL), tries to maximize the HC and minimize the operating costs while interacting with a passive electricity market and an EV aggregator. The aggregator, in the lower level, aims to maximize their profit by interacting with the primary DISCO in the UL and the rival DISCO, to satisfy the EV owners. The Karush–Kuhn–Tucker conditions are used to recast the model into an equivalent single level. The proposed framework is tested on a 33-node distribution network. Results show how an interactive-based framework can contribute to maximizing the HC of DGs. [ABSTRACT FROM AUTHOR]

• 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]

In this paper, a mixed-integer linear programming model is proposed to optimize hybrid electric vehicle (HEV) navigation modes on the city map, namely the problem of the optimal selection of navigation modes (OSNMs). The OSNMs problem of the HEV as part of the operating strategy is obtained considering a constraint set related to CO2 emissions reduction, efficient battery charging, and the optimal scheduling of deliveries. Uncertainties in the HEV navigation on urban roads are modeled using probability values assigned to an established set of traffic density values according to the levels of service. The model is implemented in a mathematical programming language (AMPL) and solved using the commercial solver CPLEX. The case study considers real data related to the Prius Prime technology and shows the effectiveness of automating the HEV navigation modes considering CO2 emissions reduction levels during an operating strategy. [ABSTRACT FROM AUTHOR]

Energy storage systems (ESS) have adopted a new role with the increasing penetration of electric vehicles (EV) and renewable energy sources (RES). EV introduce new charging demands that change the traditional demand profiles and RES are characterized by their high variability. This paper presents a new multistage distribution expansion planning model where investments in distribution network assets, RES, ESS, and EV charging stations are jointly considered. The charging demand necessary for EV transportation is performed using a vehicle model based on travel patterns. The variability associated with RES along with the demand requires the incorporation of uncertainty, which is characterized through a set of scenarios. These scenarios are generated by the ${k}$ -means++ clustering technique that allows keeping the correlation in the information of the uncertainty sources. The resulting stochastic program is driven by the minimization of the present value of the total expected cost including investment, maintenance, production, losses, and non-supplied energy. The associated scenario-based deterministic equivalent is formulated as a mixed-integer linear program, which can be solved by commercial software. Numerical results are presented for an illustrative 54-node test system. [ABSTRACT FROM AUTHOR]

The massive development of energy storage systems (ESSs) may significantly help in the supply--demand balance task, especially under the existence of uncertain and intermittent sources of energy, such as solar and wind power. Using ESSs as complements of renewable generation has technical and economic consequences in both the short-term operation and the long-term expansion planning of the grid. The authors propose a transmission expansion planning model that incorporates decisions about the expansion of generic ESS units in order to study the interaction between the penetration and location of ESSs and the transmission investment decisions. The problem is formulated as a mixed-integer linear programming model and considers different demand blocks and their correlation with renewable generation, to account for the distinct features of the system over time. The authors' results show that ESSs are not only substitutes of transmission assets, but they may also be complemented with transmission assets depending on the power system characteristics. They use a 27-bus representation of the main Chilean network to illustrate the model proposed and to highlight some interesting results about the potential complementarity of ESSs and transmission expansion. [ABSTRACT FROM AUTHOR]

This paper presents a bi-level model for distribution network and renewable energy expansion planning under a demand response (DR) framework. The role of DR has recently attracted an increasing interest in power systems. However, previous models have not been completely adapted in order to treat DR on an equal footing. The target of the distribution network and generation planner, modeled through the upper-level problem, is to minimize generation and network investment cost while meeting the demand. This upper-level problem is constrained by the lower-level problem, stressing the importance of integrating DR to time-varying prices into those investment models. The objective function considered for the lower level is the minimization of overall payment faced by the consumers. Using the Karush-Kuhn-Tucker complementarity constraints, the proposed bi-level model is recast as a mixed-integer linear programming problem, which is solvable using efficient off-the-shelf branch-and-cut solvers. Detailed results from an insular case study (La Graciosa, Canary Islands, Spain) are presented. [ABSTRACT FROM PUBLISHER]

In deregulated electricity markets, producers offer their energy to the day-ahead market. As the subsidies for renewable producers are becoming lower and lower, they have to adapt to market prices. This paper models the energy trading in the day-ahead market for wind power producers. Different strategies are proposed for this purpose: 1) several wind farms offering their energy separately to the day-ahead market; 2) the same strategy as in 1) but compensating the imbalance among different wind farms; and 3) a joint offer involving several wind farms through an external agent in order to minimize the imbalances between the offer and the final power generation. The strategies are modeled with stochastic mixed integer linear programming and Conditional Value at Risk is used to consider the risk assessment. The expected profit including risk aversion is maximized for each wind power producer and for the set of wind power producers in the case of a joint offer. A comparison of the different cases is described in detail in a case study and relevant conclusions are provided. [ABSTRACT FROM PUBLISHER]

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 model to obtain the optimal allocation and timing of renewable distributed generation under uncertainty is proposed as part of distribution expansion planning. The problem is formulated using a stochastic two-stage multiperiod mixed-integer linear programming (MILP) model, where investment decisions are done in the first stage and scenario-dependent operation variables are solved in the second stage. The model aims to minimize renewable distributed generation (photovoltaic and wind) investment costs, substation expansion investment cost, operation and maintenance costs, energy losses cost, and the cost of the power purchased from the transmission system. Active and reactive power flow equations are linearized and constraints include voltage limits, substation and feeders capacities, renewable generation limits, and investment constraints. The model is tested on a 34-bus system and conclusions are duly drawn. [ABSTRACT FROM PUBLISHER]

A coordinated strategy between wind and reversible hydro units for the midterm planning that reduces the imbalance of wind power and improves system efficiency is proposed. A stochastic mixed integer linear model is used, which maximizes the joint profit of wind and hydro units, where conditional value at risk (CVaR) is used for model risk. The offering strategies studied are 1)separate wind and hydro pumping offer, where the units work separately without a physical connection and 2)a single wind and hydro pumping offer with a physical connection between them to store wind energy for future use. The effects of a coordinated wind-hydro strategy for midterm planning are analyzed, considering CVaR and the future water value. The future water value in the reservoirs is analyzed hourly for a period of 1 week and 2 months, in two realistic case studies. [ABSTRACT FROM PUBLISHER]

Wind and hydro technologies represent an important part of the electricity generation sector. However, there have been few detailed studies that have investigated the synergies resulting from their combined operation. To address that, we formulate three optimization models where wind and reversible hydro technologies bid in a day-ahead market. The bidding strategies are divided into three categories: 1) separate wind and reversible hydro offers without a physical connection between them, 2) separate wind and reversible hydro offers with a physical connection to store by pumping the wind energy surplus, and 3) single wind and reversible hydro offers with a physical connection. Risk is considered in the models by means of the conditional value at risk (CVaR). A comparison of the models and relevant conclusions are drawn from an illustrative case study of the Iberian day-ahead electricity market. [ABSTRACT FROM PUBLISHER]

Wind power generators face risks derived from fluctuations in market prices and variability in power production, generated by their high dependence on wind speed. These risks could be hedged using weather financial instruments. In this research, we design and price an up-and-in European wind put barrier option using Monte Carlo simulation. Under the existence of a structured weather market, wind producers may purchase an up-and-in European wind barrier put option to hedge wind fluctuations, allowing them to recover their investments and maximise their profits. We use a wind speed index as the underlying index of the barrier option, which captures risk from wind power generation and the Autoregressive Fractionally Integrated Moving Average (ARFIMA) to model the wind speed. This methodology is applied in the Colombian context, an electricity market affected by the El Niño phenomenon. We find that when the El Niño phenomenon occurs, there are incentives for wind generators to sell their energy to the system because their costs, including the put option price, are lower than the power prices. This research aims at encouraging policymakers and governments to promote renewable energy sources and a financial market to trade options to reduce uncertainty in the electrical system due to climate phenomena. [ABSTRACT FROM AUTHOR]

This research tackles effective and functional management practices in energy consumption using advanced technological solutions to mitigate unexpected events. This study specifically focuses on the recent COVID-19 pandemic that led to a reduction in energy consumption and therefore meddled with the project management's status quo for the energy sources. Moreover, this research analyzes the role of information and communication technology (ICT) solutions in energy management before and after the recent coronavirus pandemic turmoil. This study also examined the role and place of sustainable energy resources in the effective management of energy consumption during the COVID-19 pandemic. The main purpose of this research is to demonstrate how the recent coronavirus pandemic affected energy consumption and energy prices in many countries and how it paved the way for the new ICT in managing energy consumption. This study elaborated a model to show how these changes evolved in selected European Union (EU) Member States. It turns out that adjusted electricity consumption, as well as the average daily spot electricity prices for the baseload power in the EU countries, declined, and this trend is likely to continue in the next years to come. Due to the COVID-19 pandemic, substantial changes in the economy led to reduced office space usage, cutting commuting and business trips, and bringing commercial travel almost to a halt, further shifting the energy balance towards renewable energy sources (RES). Besides, it becomes apparent that ICT solutions used in maintaining and superintending the energy balance are gaining popularity and wide recognition worldwide and will help pave the way towards modern and smart high-renewables grids. [ABSTRACT FROM AUTHOR]

In this paper, a multiobjective approach to carry out the planning of medium-voltage (MV) and low-voltage (LV) distribution systems, considering renewable energy sources (RES) and robustness, is proposed. Due to the uncertainties associated with RES and demand, the proposed planning methodology takes into account a robust planning index (RPI). This RPI allows us to evaluate the robustness estimation associated with each planning solution. The objective function in the mathematical model considers the costs of investment and operation and the robustness of the planning proposals. Due to the computational complexity of this problem, which is difficult to solve by means of classical optimization techniques, MV/LV planning is solved by a decomposition search and a general variable neighborhood search (GVNS) algorithm. To demonstrate the efficiency and robustness of this methodology, tests are performed in an integrated distribution system with 50 MV nodes and 410 LV nodes. Our numerical results show that the proposed methodology makes it possible to minimize costs and improve robustness levels in distribution system planning. [ABSTRACT FROM AUTHOR]

A model suitable to obtain where and when renewable energy sources (RES) should be allocated as part of generation planning in distribution systems is formulated. The proposed model starts from an existing two-stage stochastic mixed-integer linear programming (MILP) problem including investment and scenario-dependent operation decisions. The aim is to minimize photovoltaic and wind investment costs, operation costs, as well as total substation costs including the cost of the energy bought from substations and energy losses. A new Benders' decomposition framework is used to decouple the problem between investment and operation decisions, where the latter can be further decomposed into a set of smaller problems per scenario and planning period. The model is applied to a 34-bus system and a comparison with a MILP model is presented to show the advantages of the model proposed. [ABSTRACT FROM AUTHOR]

Highlights • Proposing simultaneous pro-active and reactive approaches to model the uncertainty. • Analyzing the islanded and grid-connected microgrid cases for one year period. • Considering space heating and cooling load via two-capacity building thermal model. • Probing the flexibility of HVAC coordinated with thermal masses, EWH, and BESS. Abstract This paper proposes a novel energy management framework by combining proactive and reactive approaches to efficiently address the uncertainties associated with generation and demand in islanded and interconnected operation of residential microgrid (MG). The MG considers renewable energy sources (RESs), a diesel generator (DG), and a storage device with the possibility of power exchanges with the grid. The proposed formulation is cast as stochastic mixed-integer linear programming (MILP) model with 24-hour rolling horizon, simulated periodically by updating input data and advancing on an hourly basis for a one year scheduling period. The objective is to minimize the total MG cost while preserving comfort priorities of individual households. The energy balance is potentiated by smart central management system of heating, ventilation and air-conditioning and electric water heater loads in conjunction with exploiting the building thermal inertia of well-insulated buildings. Random outages of MG components are modeled via a two-state Markov chain process, which is considered in the reactive approach. Extensive numerical results are presented to reveal the benefits obtained by coordinating the demand response and supply sources for a practical case of Helsinki, Finland. Finally, the impact of various system parameters on the optimal expected MG cost and risk indices is investigated. [ABSTRACT FROM AUTHOR]

• Proposing and developing a planning tool for MV/LV large-scale distribution systems. • Proposing distribution systems planning via parallel and decomposition techniques. • Proposing RES and ESS allocation in MV/LV large-scale distribution systems planning. The world energy demand is increasing. Currently, most electricity generation sources are fossil fuels. In recent years, the global energy matrix for power generation is changing, in order to meet the demand with minimal environmental impacts. In this context, a new approach to solve the medium- (MV) and low- voltage (LV) planning of large-scale distribution systems via parallel computing and decomposition techniques is proposed. The mathematical formulation considers the allocation of substations, distribution transformers, MV and LV circuits, support structures, poles, renewable energy sources (RES), and energy storage sources (ESS). In addition, variable costs related to the operation of RES and ESS, power losses in cables, distribution transformers and substations, energy purchased from substations, and greenhouse gas emissions are also taken into account. System reliability and maintenance costs of these devices are also considered in the planning. To evaluate the new methodology performance, tests in a large-scale distribution system with 200 nodes in MV and 1672 nodes in LV is considered. Numerical results show the proposed methodology is able to find good solutions that guarantee the minimization of planning costs considering RES and ESS allocation. Furthermore, system reliability is improved by up to 22% and greenhouse gas emissions mitigation by up to 18%. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a novel strategy, optimized by genetic algorithms, to control stand-alone hybrid renewable electrical systems with hydrogen storage. The strategy optimizes the control of the hybrid system minimizing the total cost throughout its lifetime. The optimized hybrid system can be composed of renewable sources (wind, PV and hydro), batteries, fuel cell, AC generator and electrolyzer. If the renewable sources produce more energy than the one required by the loads, the spare energy can be used either to charge the batteries or to produce H2 in the electrolyzer. The control strategy optimizes how the spare energy is used. If the amount of energy demanded by the loads is higher than the one produced by the renewable sources, the control strategy determines the most economical way to meet the energy deficit. The optimization of the various system control parameters is done using genetic algorithms. This paper explains the strategy developed and shows its application to a PV–diesel–battery–hydrogen system. [Copyright &y& Elsevier]

• MILP formulation for distribution systems planning with voltage-dependent models. • Allocation of energy storage and renewable distributed generation units. • CO 2 emissions regulation via cap policy for the distribution system. • k-means ++ method is applied to reduce the number of scenarios of uncertain parameters. • Impact analysis of voltage-dependent load models and network reconfiguration. This work proposes a novel mixed-integer linear programming model to address the medium-term reinforcement planning for active distribution networks, taking into account multiple investment options and CO 2 emission limits. The investment plan jointly includes (i) the replacement of overloaded conductors, (ii) the installation of voltage control equipment such as voltage regulators and capacitor banks, and (iii) the installation of distributed energy resources, such as dispatchable and non-dispatchable renewable generators, and energy storage units. Uncertainties associated with the demand for electricity, energy prices at the substation, and non-dispatchable distributed generation are addressed through scenario-based stochastic optimization. In contrast to conventional planning methods, the proposed approach models the load as voltage-dependent in order to achieve substantial reductions in energy consumption. As another outstanding feature, network reconfiguration, which is an operational planning alternative that is normally addressed independently, is incorporated within the planning options. The objective function of the model is aimed at establishing an investment strategy with minimal total costs, but that satisfies the operational restrictions of the network and CO 2 emissions cap. A 69-node system was used to test the proposed model and, the results show that modeling the load as voltage-dependent and integrating network reconfiguration into the medium-term planning actions helps to achieve an effective network that, in addition to being environmentally friendly, has low total planning costs. Finally, the scalability of the proposed method was evaluated using a real 2313-node system. [ABSTRACT FROM AUTHOR]