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New entities and ways of operating electrical power systems have emerged. An example of this is the microgrid (MG), which can be described as a set of power generation units, energy storage systems (ESSs) and demands that can operate as a single set, in order to optimize their energy resources. Moreover, the recent growth in the use of electric vehicles (EVs) in transportation systems has created new challenges to electrical power systems. This article proposes a two-stage stochastic energy scheduling model for an MG to set up the day-ahead optimal decision in the first stage. Real-time operations, depending on wind and photovoltaic solar power, baseload demand and EV demand variability, are taken into account in the second stage. The proposed model considers conventional generators and alternating current (AC) linearized power flow constraints. Besides, a detailed ESS operation is considered, including degradation costs, and nonlinear charging/discharging as well as efficiency variation dependent of state of charge (SOC), for lead-acid and lithium-ion technologies. EV demand behavior is generated by probability-based algorithms that use the Monte Carlo method to perform their simulations, which are based on data of initial SOC, plugin/out times, type of EVs, and chargers. Finally, the proposed model is successfully validated with an IEEE-37 bus system. The results support the superiority of the proposed model over existing works, showing significant variations in battery bank (BB) operation. The computational time is similar to previous works, despite adding new constraints that increase the complexity. [ABSTRACT FROM AUTHOR]

This paper proposes a trilateral bilevel stochastic mixed integer bilevel linear programming (MIBLP) model to handle a joint master-slave operation-planning problem. This model considers the interactions among an integrated community energy system (ICES), prosumers, and the wholesale electricity market (WM). The ICES, in the upper level, makes a joint optimal photovoltaic (PV) and energy storage system plan taking into account the concurrent interactions with the WM and prosumers to maximize its profit. On the other hand, in the lower level, the prosumers aim at minimizing their bills via an optimal PV-package sizing while interacting with the ICES and the WM simultaneously. The strong duality theorem is used to recast this MIBLP model into an equivalent single-level model. Since the lower level is also an operation-planning problem, this recast results in a mixed integer nonlinear programming (MINLP) model that prevents finding a satisfactory solution. To remedy this issue, sensitivity analysis and a separation-based linearization technique are applied. Numerical results illustrate the effective performance of the proposed business model while providing valuable information for the ICES and consumers. [ABSTRACT FROM AUTHOR]

System operators still rely on deterministic criteria such as the N−1 (i.e. the system would be able to withstand the outage of any single component without any load shedding) to hedge the system against contingencies. While simple and practical, this criterion miscalculates the actual amount of reserve required since it ignores the probability of contingency occurrence. Therefore, this criterion may lead to suboptimal reserve procurement and economic performance of the system. This study presents a multiperiod probabilistic security‐constrained unit commitment (UC) model that includes the probabilities of generation and transmission contingencies for optimal reserve sizing, sourcing, allocation, and timing. The ability of energy storage systems (ESS) to provide contingency reserve is explicitly modelled. Benders' decomposition and linearisation techniques are applied to solve the proposed probabilistic UC, which would be intractable otherwise. The impact of ESS on the contingency reserve procurement and deployment in post‐contingency states are analysed on a modified version of the IEEE One‐Area Reliability 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]

Due to the rapid growth in the amount of wind energy connected to distribution grids, they are exposed to higher network constraints, which poses additional challenges to system operation. Based on regulation, the system operator has the right to curtail wind energy in order to avoid any violation of system constraints. Energy storage systems (ESS) are considered to be a viable solution to solve this problem. The aim of this paper is to provide the best locations of both ESS and wind power by optimizing distribution system costs taking into account network constraints and the uncertainty associated to the nature of wind, load and price. To do that, we use a mixed integer linear programming (MILP) approach consisting of loss reduction, voltage improvement and minimization of generation costs. An alternative current (AC) linear optimal power flow (OPF), which employs binary variables to define the location of the generation, is implemented. The proposed stochastic MILP approach has been applied to the IEEE 69-bus distribution network and the results show the performance of the model under different values of installed capacities of ESS and wind power. [ABSTRACT FROM AUTHOR]

In case of abnormal conditions, distribution systems should be reconfigured to overcome the impacts of outages such as overloads of network components and increased power losses. For this purpose, energy storage systems (ESS) and renewable energy sources (RES) can be applied to improve operating conditions. An optimal contingency assessment model using two-stage stochastic linear programming including wind power generation and a generic ESS is presented. The optimization model is applied to find the best radial topology by determining the best switching sequence to solve contingencies. The proposed model is applied to a 69-node distribution system and the results of all possible contingencies in the network are examined considering three different case studies with several scenarios. In addition, a reconfiguration analysis including all the contingencies is presented for the case studies. [ABSTRACT FROM PUBLISHER]

Intermittence and variability of renewable resources is often a barrier to their large scale integration into power systems. We propose a stochastic real-time unit commitment to deal with the stochasticity and intermittence of non-dispatchable renewable resources including ideal and generic energy storage devices. Firstly, we present a mathematical definition of an ideal and generic storage device. This storage device definition has some mathematical advantages: 1) it can be easily integrated within complex optimization problems, 2) it can be modeled using linear programming, suitable for practical large-scale cases. Secondly, a stochastic unit commitment with ideal and generic storage devices and intermittent generation is proposed to solve the joint energy-and-reserves scheduling and real-time power balance problem, reflecting the minute-by-minute intermittent changes and the stochasticity of renewable resources. We also compare our results with those obtained using a deterministic unit commitment with perfect information. The proposed model is illustrated with a 24-bus system. [ABSTRACT FROM AUTHOR]

Energy storage system (ESS) has been advocated as one of the key elements for the future energy system by the fast power regulation and energy transfer capabilities. In particular, for distribution networks with high penetration of renewables, ESS plays an important role in bridging the gap between the supply and demand, maximizing the benefits of renewables and providing various types of ancillary services to cope the intermittences and fluctuations, consequently improving the resilience, reliability and flexibility. To solve the voltage fluctuations caused by the high permeability of renewables in distribution networks, an optimal capacity allocation strategy of ESS is proposed in this paper. Taking the life cycle cost, arbitrage income and the benefit of reducing network losses into consideration, a bilevel optimization model of ESS capacity allocation is established, the coordination between active/reactive power of associate power conversion system is considered, and the large scale nonlinear programming problem is solved using genetic algorithm, simulated annealing and mixed integer second-order cone programming method. The feasibility and effectiveness of the proposed algorithm have been verified. [ABSTRACT FROM AUTHOR]

As a large number of distributed generations are connected to the distribution system, research on the hosting capacity is actively being conducted. In particular, various methods, such as smart inverter functionality, co-located energy storage systems (ESS), and the use of on-load tap changers (OLTC), have been proposed to improve the hosting capacity. In this paper, a method to improve the hosting capacity by utilizing a solid-state transformer (SST) and its unique control capability is proposed. Lastly, the proposed method is verified in the distribution system of the Republic of Korea using the OpenDSS program. [ABSTRACT FROM AUTHOR]

Energy hub (EH) is a concept that is commonly used to describe multi-carrier energy systems. New advances in the area of energy conversion and storage have resulted in the development of EHs. The efficiency and capability of power systems can be improved by using EHs. This paper proposes an Information Gap Decision Theory (IGDT)-based model for EH management, taking into account the demand response (DR). The proposed model is applied to a semi-realistic case study with large consumers within a day ahead of the scheduling time horizon. The EH has some inputs including real-time (RT) and day-ahead (DA) electricity market prices, wind turbine generation, and natural gas network data. It also has electricity and heat demands as part of the output. The management of the EH is investigated considering the uncertainty in RT electricity market prices and wind turbine generation. The decisions are robust against uncertainties using the IGDT method. DR is added to the decision-making process in order to increase the flexibility of the decisions made. The numerical results demonstrate that considering DR in the IGDT-based EH management system changes the decision-making process. The results of the IGDT and stochastic programming model have been shown for more comprehension. [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]

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]

• Development of a MILP model for the optimal operating scheme of NESCs. • An efficient scheme of NESCs to improve power grid performance through the LF. • The CPLEX solver is used to find the optimal solution of this proposed MILP model. • Operational and financial gains are obtained by scheduling loads and using SBs. In Smart Cities (SC), the efficient management of services such as health, transport, public safety, and especially the electricity ensures the welfare of citizens. In recent years, the insertion of renewable sources (RSs) (e.g., solar and wind) in the power grid (PG) of SCs has contributed to meeting the electricity needs of the various consumer units. However, the large-scale integration of these RSs can fatigue the assets, leading to their premature aging and, consequently, compromising the quality of electricity supply. To overcome these challenges, the implementation of Neighboring Energy Storage Communities (NESCs) employing demand response (DR) strategies along with efficient coordination of storage batteries (SBs) could be a promising alternative. In this sense, the present work proposes a mixed-integer linear programming (MILP) model to efficiently manage SBs and the set of household appliances, including charging electric vehicles (EVs), in an NESC provided solely by PG. The proposed model aims to minimize: the total costs related to energy consumption, the peak rebound effect on the total consumption profile, energy wastage through load factor (LF) improvement, and the deep discharges in the SBs during their daily operational cycle. Operational constraints related to the home appliances, such as average usage time, the number of times that the appliance is used daily, etc., are taking into account. The EV state-of-charge (SOC), EV charging rate limits, and initial and final SOC of the SBs, are also considered. A Monte Carlo Algorithm (MCA) is used to simulate the habitual consumption patterns of each customer. The proposed model was implemented in AMPL and solved using CPLEX. The performance of this proposed model is evaluated considering two NESCs differentiated by the number of consumer communities. A first NESC (small-scale) is analyzed considering only two consumer communities. In this NESC, two case studies (Case 1 and 2) are discussed. Next, the second NESC (large-scale) that considers 14 consumer communities is analyzed for the most complete case study (Case 2). Within each NESC, consumer communities are differentiated by the household income and the types of SBs (individual and shared) that support each community. The results corroborate the applicability of the MILP model to real case studies on a diverse scale, guaranteeing the efficient use of PG at the same time that each SB seeks the most optimized operation. [ABSTRACT FROM AUTHOR]

• 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]