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A generic operations framework for a distribution company (disco) operating in a competitive electricity market environment is presented in this paper. The operations framework is a two-stage hierarchical model in which the first deals with disco's activities in the day-ahead stage, the day ahead operations model (DAOM). The second deals with disco's activities in real-time and is termed real-time operations model (RTOM). The DAOM determines the disco's operational decisions on grid purchase, scheduling of distributed generation (DG) units owned by it, and contracting for interruptible load. These decisions are imposed as boundary constraints in the RTOM and the disco seeks to minimize its short-term costs keeping in mind its day-ahead decisions. A case-study is presented considering the well-known 33-bus distribution system and three different scenarios are constructed to analyze the disco's actions and decision-making in this context.

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... There is a direct relationship between customers' power consumption and the electricity price. Thus, in this type of load, selecting a demand response program is of great importance [3]. An instantaneous pricing tariff program should be designed such that the operation costs are reduced and the distribution network reliability is increased. ...

... The investigated studies had some shortcomings that might affect the decisions of a DSO. In this regard, in [3,[9][10][11][12][13], although DR programs were used, the reliability of the distribution system was not investigated. In some articles (for example, [12][13][14][15][16][17][18]), the authors presented a framework for developing sell/purchase strategies for a distribution company in the energy and retail markets considering demand uncertainty, but none of them examined the effect of elasticity on consumption demand. ...

... In this equation, P Grid t is the purchased power from grid at each hour and Pr IL t is the real-time price of interruptible loads. We considered Pr IL t to be 10% of the real-time price of purchasing electricity from the grid according to [3]. P IL,C t is the interruptible load contract, interruptible loads being those that participate in the interruptible/curtailable program. ...

This paper presents an innovative instantaneous pricing scheme for optimal operation and improved reliability for distribution systems (DS). The purpose of the proposed program is to maximize the operator’s expected profit under various risk-taking conditions, such that the customers pay the minimum cost to supply energy. Using the previous information of the energy consumption for each customer, a customer baseline load (CBL) is defined; the energy price for consumption costs higher and lower than this level would be different. The proposed scheme calculates the difference between the baseline load and the consumption curve with the electricity market price instead of calculating the total consumption of the customers with the unstable price of the electricity market, which is uncertain. In the proposed tariff, the developed cost and load models are included in the distribution system operation problem, and the objective function is modeled as a mixed integer linear programming (MILP) problem. Also, the effect of demand response (DR) and elasticity on the load curve, the final profit of the distribution system operator, and payment risk and operation costs are examined. Since there are various uncertainties in the smart distribution grid, the calculations being time-consuming and volumetric is important in the evaluation of reliability indices. Thus, when computation volume can be decreased and computation speed can be increased, analytical reliability analysis methods can be used, as they were in the present work. Finally, the changes in the reliability indices were calculated for the ratio of the customers’ sensitivity to the price and the customers’ participation in the proposed tariff using an analytical method based on Monte Carlo simulation (MCS). The results showed the efficiency of the proposed method in increasing the operator profit, reducing the operation costs, and enhancing the reliability indices.

... Similarly, in [15], a model of the DisCo for participating in the DA market is presented in two stages, aiming at minimizing the total operation costs. In [16], a short-term two-stage decision model is presented for a DisCo, in which both DA and RT markets, as well as DG units and Load Curtailment (LC), are considered, with the objective of minimizing the operation costs. In [17], a nonlinear short-term stochastic model is presented for a DisCo in the presence of voltage-sensitive loads. ...

... The reviewed papers have weaknesses that can affect the distribution company's decisions. In this respect, in [6,[14][15][16][17][18][19], the DisCo's decision problem is expressed by a single-level model. By considering the DisCo's position in energy markets as a strategic player, these models cannot accurately reflect the behavior of a DisCo. ...

... Besides, in some studies such as [2,32,33] the physical constraints of the transmission lines have not been considered. In [14][15][16][17][18][19], as well as [34], the model mentioned as a single-level optimization problem, neglecting the influence of company on the energy market. In addition, in some studies, DisCo has an influence on aggregators or MGs, and interact with them for its profit or have an influence on consumers for more DR (e.g. in [4,10,11,[24][25][26]31]). In some studies, RTP and LC program, as well as elastic demands, as alternatives for DR program, are not considered (e.g. in [3,35,36]). ...

Electricity retailers encounter several uncertainties in a pool-based electricity market, such as uncertainties in demand and market price. This paper presents a new stochastic model for an electricity retailer with flexible demands to facilitate its energy procurement. The retailer, in addition to participating in the wholesale market, can exchange with a wind producer through a contract under the new short-term trading mechanism. In order to facilitate energy and financial exchanges, to estimate the spot market price, and to send contract information to system operator, the short-term trading operator is introduced. Following the day-ahead market clearing, the wind producer and the retailer submit their decisions on the contract to the introduced operator. The operator allocates possible energy transactions based on the surpluses or shortages of the parties. The uncertainty associated with market prices, demand, and the exchangeable power within short-term trading mechanism are considered by sets of scenarios. Also, profit risk is considered by conditional value at risk. The resulting stochastic linear formulation is applied to two case studies to show the effectiveness of the proposed model. Results indicate that the proposed mechanism increases the profit of the retailer during the short-term operation.

... Similarly, in [15], a model of the DisCo for participating in the DA market is presented in two stages, aiming at minimizing the total operation costs. In [16], a short-term two-stage decision model is presented for a DisCo, in which both DA and RT markets, as well as DG units and Load Curtailment (LC), are considered, with the objective of minimizing the operation costs. In [17], a nonlinear short-term stochastic model is presented for a DisCo in the presence of voltage-sensitive loads. ...

... The reviewed papers have weaknesses that can affect the distribution company's decisions. In this respect, in [6,[14][15][16][17][18][19], the DisCo's decision problem is expressed by a single-level model. By considering the DisCo's position in energy markets as a strategic player, these models cannot accurately reflect the behavior of a DisCo. ...

... Besides, in some studies such as [2,32,33] the physical constraints of the transmission lines have not been considered. In [14][15][16][17][18][19], as well as [34], the model mentioned as a single-level optimization problem, neglecting the influence of company on the energy market. In addition, in some studies, DisCo has an influence on aggregators or MGs, and interact with them for its profit or have an influence on consumers for more DR (e.g. in [4,10,11,[24][25][26]31]). In some studies, RTP and LC program, as well as elastic demands, as alternatives for DR program, are not considered (e.g. in [3,35,36]). ...

Adopting strategic behavior in the wholesale electricity market can revolutionize the performance of the Distribution Company (DisCo) in the operation of the distribution network. Besides, the demand response program in real-time pricing (RTP) environment, which is practicable within the smart distribution network, has a significant positive impact on the strategic behavior of this company. In this paper, a new framework is proposed to develop the sell and purchase strategies for a strategic distribution company in the energy and retail markets. The DisCo in this paper is the owner and operator of the distribution system that can affect the price of the energy due to the ownership of conventional and energy storage systems (ESSs). The uncertainty associated with the demands within the distribution network is considered by sets of scenarios. Also, the elasticity of the demands, which pertains to the retail price, is taken into account in the demand response program. Retail energy prices are also determined for customers under the RTP scheme. The problem is modeled in the form of a bi-level optimization problem, the upper-level of which includes maximizing the profit from energy sales to consumers under the RTP scheme and managing the production of distributed generation (DG) units, amount of charging or discharging of the storage system and deciding about LC program. And the lower-level is a market-clearing of the wholesale market aiming at maximizing social welfare. The upper network (sub-transmission network) is modeled in the form of a DC load flow equations to consider the impact of power transmission limits. By converting the bi-level problem to MPEC model and linearizing it using the dual theory and KKT conditions to a MILP model, finally, the MILP model is solved using the GAMS software. The performance of the proposed model is shown in two case studies, based on the IEEE-33BUS distribution network, and the 3-bus and 14-bus sub-transmission networks. Simulations investigate the impact of the strategic behavior of the DisCo on the financial and technical aspects of its operations. Simulations also indicate that the proposed model is an appropriate tool for analyzing the performance of the strategic DisCo in the wholesale and retail energy markets. Also, using the proposed approach can result in increasing of DisCo’s profit, while declining in wholesale prices and load interruptions.

... The DISCO's decision maker role is very important for efficient operation from technical and economical points of view, as these decisions improve market operations like competition and technical operations like reliability and service quality. 3 With the integration of DG units, distribution networks have transformed from a passive state to an active state as in transmission networks. 4 A few operating methods such as nodal pricing, 5 which is used in transmission networks in a deregulated environment, are also applicable in active distribution networks. ...

... 6 Locational marginal price (LMP) is the most effective method to determine nodal price in practice. 3,7 Various papers are available in the literature for the computation of LMP in distribution networks. The comparison of research contribution by various authors in addressing the different features considered for LMP computation in a distribution system are shown in Table 1. ...

... The generation of each unit has been computed based on cost coefficients of that generator and LMP at the DG bus using Equations 1 and 2; the loss reduction from the base case has been computed using Equation 3. ...

... Porém, as transações realizadas no mercado RT podem ser bastante arriscadas em virtude da alta volatilidade dos preços de equilíbrio de mercado RT, acarretando em perdas monetárias significativas [Wang et al 2017]. Assim sendo, é fundamental que as transações da DisCo sejam concentradas na operação DA (ou seja, no mercado DA) e que os mercados RT sejam utilizados apenas para os ajustes necessários para acomodar os desvios das quantidades de energia adquiridas no mercado DA [Algarni e Bhattacharya 2009], [Nyiso 2016], [Stoft 2002]. ...

... Nos últimos anos, a crescente integração de recursos energéticos distribuídos (DER) assim como a intensificação dos esforços para implementação das smart grids têm elevado o interesse de pesquisadores no problema de planejamento da operação da DisCo [Algarni e Bhattacharya 2009] [Safdarian et al. 2014] [Wang et al 2017]. Os modelos de operação DA e RT são, normalmente, formulados e resolvidos separadamente de forma sequencial, semelhantemente ao realizado nos sistemas de potência atuais em que os mercados DA e RT são operados em sequência [Stoft 2002]. ...

... Neste trabalho propõe-se uma metodologia pseudodinâmica baseada na metaheurística Busca Tabu (BT) para solução do problema de planejamento da operação de curto prazo de redes de distribuição, com base na perspectiva da DisCo [Cerbantes 2017]. Uma abordagem probabilística sequencial é proposta, considerando mercados DA e RT [Algarni e Bhattacharya 2009]. As decisões do estágio DA são otimizadas para maximização dos lucros, enquanto que a DisCo busca minimizar os custos operacionais no estágio RT. ...

This paper presents a probabilistic sequential decision framework for the short-term operation planning of distribution networks participating in day-ahead (DA) and real-time (RT) markets. The problem is modeled assuming the perspective of the distribution company (DisCo) and solved through a pseudo-dynamic Tabu Search (TS)-based solution algorithm. The DisCo’s operational decisions are firstly optimized in a DA operation stage aiming to maximize profits, and then in RT to minimize the adjustments that are required to accommodate deviations from forecasted quantities. The voltage-sensitiveness of power load injections and demand related constraints are explicitly formulated. In addition, the network is modeled using full ac power flow equations. The resulting models are characterized as large-scale non-linear non-convex mathematical programs with continuous and discrete variables. Numerical results show the effectiveness of the proposed approach.

... A bi-level optimization approach is developed in [15,16] to achieve the optimal decisions of the Disco in the presence of DER aggregators. A two-stage optimization approach is introduced in [17,18] to formulate the behavior of a Disco, as a price-taker player, in the DA and RT markets. ...

... A stochastic approach to derive optimal bidding strategies for a wind power producer and energy storage in the Spanish multi-stage market, consisting of DA, intraday, and RT markets, is proposed in [20]. Generally stated, in [6][7][8][9][10][11][12][13][14][17][18][19][20][21][22], Disco is considered as a price-taker player in the DA and RT markets. In presence of DERs, Discos further contribute in markets and thus the markets' outcomes may change. ...

... The Disco operation cost in the second-stage decision making process (TC RT ω ) is modeled by Eq. (17). It consists of cost of exchanged power with the RT market, costs/revenues due to power deviation of the RESA ...

In Active Distribution Networks (ADNs), Distribution Company (Disco) follows two main strategies of dispatching of Distributed Energy Resources (DERs) and trading energy with wholesale energy markets, including Day-Ahead (DA) and Real-Time (RT) markets, to meet the demand. An attempt is made in this paper to model the strategic behavior of the Disco, in the wholesale DA and RT energy markets, through a bi-level optimization approach While the objective of the upper-level problem is to minimize the expected cost of the Disco, the lower-level problem (with two optimization problems) formulates to simultaneously maximize the social-welfare of the DA market and minimize the cost of the RT market. Furthermore, uncertain behavior of renewable energy sources as well as demand is tackled into the problem formulation. To this end, Disco decision-making represents as a risk-based two-stage stochastic problem where the Disco’s risk aversion is modeled using conditional value at risk (CVaR) method. Generally stated, the proposed model is a non-linear bi-level problem which may be transformed into a non-linear but single-level problem through Karush–Kuhn–Tucker (KKT) conditions and dual theory. Detailed numerical results on a 6-bus and RTS 24-bus power systems are used to demonstrate efficiency of the proposed model. Moreover, sensitivity analysis is carried out to investigate the effect of risk-aversion parameter on the decision making of the Disco and the offers/bids in both the DA and RT markets.

... The increasing integration of distributed energy resources (DERs) and the current efforts on grid modernization motivated the research on the DisCo's operation planning problem [6]- [10]. A two-stage decision framework is presented in [6], [7]. ...

... The increasing integration of distributed energy resources (DERs) and the current efforts on grid modernization motivated the research on the DisCo's operation planning problem [6]- [10]. A two-stage decision framework is presented in [6], [7]. Algarni and Bhattacharya [6] rely on deterministic formulations to model the DisCo's short-term planning, whereas Safdarian et al. [7] propose an stochastic approach to address uncertainties that are inherent to the DisCo's operation. ...

... A two-stage decision framework is presented in [6], [7]. Algarni and Bhattacharya [6] rely on deterministic formulations to model the DisCo's short-term planning, whereas Safdarian et al. [7] propose an stochastic approach to address uncertainties that are inherent to the DisCo's operation. In [8], the authors propose a short-term decision model for a DisCo including price-based demand response. ...

Distribution networks have undergone fundamental changes driven mainly by the rapid penetration of distributed energy resources (DERs). The distributed power sources connected to the distribution wires (often through power inverters) can provide not just active power, but also reactive power support to the grid. Reactive power management is a key aspect that may substantially affect the efficiency and the quality of supply of distribution networks, and as a consequence the operation of a distribution company (DisCo). This work proposes the integration of an enhanced version of a practical and transparent nodal reactive power pricing into a precise and complete short-term operation model of a DisCo. This model explicitly considers distribution-network-related technical constraints such as dispatchable and intermittent DG units, voltage-sensitive loads, time-varying retail prices, stationary batteries, step voltage regulators, shunt capacitor banks, and demand-related constraints. The resulting model is characterized as a large-scale highly nonlinear and nonconvex program with continuous, binary, and discrete variables. To solve such problem, a pseudo-dynamic Tabu Search (TS)–based algorithm is proposed and effectively tested. IEEE

... In this paper, the DISCO is playing a dual role of being a retailer and bearing the responsibility of network operation. Thus, the DISCOs try to operate the distribution system by maintaining its stability and reliability, while maximizing profits [26]. In contrast, the building end-users are separate from the DISCO, as independent and autonomous players in the electricity market. ...

... Equation (27) is calculated based on (25) immediately after. When we cannot achieve or calculate the past and time-delayed values such as C 0 , C -1 , C -2 , …, P 0 , P -1 , P -2 , …, S 0 , S -1 , S -2 , …, the first-time values such as C 1 , P 1 , S 1 , assumed to be known on day d-1, replace them in (24), (26), (28) respectively. Overall, (29) lists all types of input variables concluding the time value t, before the data-normalization in (7). ...

The distribution company (DISCO) determines optimal retail prices to operate the distribution network efficiently while promoting demand response (DR) programs. In addition, an energy storage system (ESS), which improves peak load management, is widely used for price-based DR. This paper proposes an electricity retail pricing strategy that considers the optimal operation of an ESS using a machine learning algorithm. An artificial neural network (ANN) is used to develop a practical model of the DR scheduling of an ESS. This model is trained using historical data that include the electricity price and the corresponding optimal demand obtained from the building energy management system. The proposed model is replicated using mathematical equations and directly integrated into the constraints of the retail pricing optimization problem of the distribution management system. The proposed ANN-based DR model of the ESS allows the development of an optimal pricing strategy with a single-level structure while reflecting the decision-making process of both the DISCO and the building operator. The proposed ANN-based DR model is verified through case studies, which prove that the model successfully expresses the price-optimal demand function and has high practical applicability. The results of the retail pricing demonstrate that the proposed strategy can accurately determine the balancing points while reducing the peak load.

... In addition to EVAs, other options which are considered for the provision of flexibility in distribution systems include hourly load curtailments, which can neither shift loads to hours with lower energy prices nor be effective when energy supply is sufficiently available [18], [19]. Other cases considered distributed generators, which can only supply energy, while EVAs can both supply and consume energy [20], [21]. ...

... In some literature such as [25], DISCO cooperates with the distribution system operator, which is responsible for network operation. While, in some other literature such as [18] and [26], DISCO manages network operation by itself. The proposed model for DISCO is applicable for both cases as it is able to both consider and not consider network constraints. ...

An operation model for distribution companies (DISCOs) is proposed to reduce their operation costs by fully utilizing the flexibility of electric vehicle aggregators (EVAs). In the proposed model, linear decision rules approximation is adopted to achieve mathematical tractability, and distributionally robust optimization is applied to evaluate costs affected by uncertainties in renewable power outputs and EVA charging demands. Case studies are conducted under various settings. With the proposed model, using EVAs to mitigate uncertainties is achieved and is further classified into delaying uncertainties and eliminating uncertainties. As a result, average penalties for DISCO’s deviations from its planned energy portfolio are reduced. Besides, EVA charging demands are shifted to hours with lower energy prices to reduce energy costs of DISCO. Using EVAs to mitigate uncertainties and shifting EVA charging demands are properly coordinated under the proposed model. Moreover, power losses in EVA charging and discharging are utilized to reduce the scale of uncertainties, which decreases average penalties for energy deviations of DISCO.

... The decision-making problem of a Disco that participates as a price-taker in the electricity market is modeled in different ways. Two-stage deterministic and stochastic optimization approaches are proposed in both DAEM and real-time energy market (RTEM) in [7] and [8], respectively. Optimal scheduling of DERs by the Disco is done regarding the forecast prices of DAEM and the reserve markets in [9]. ...

... (7)- (10). The amount of feeders current is determined by (7). The upper and lower limitations of current and voltage of the network are modeled by (8). ...

Abstract—One of the emergent prospects for active
distribution networks is to establish new roles to the distribution
company (Disco). The Disco can act as an aggregator of the
resources existing in the distribution network, also when parts of
the network are structured and managed as microgrids (MGs).
The new roles of the Disco may open the participation of the
Disco as a player trading energy in the wholesale markets, as well
as in local energy markets. In this paper, the decision making
aspects involving the Disco are addressed by proposing a bi-level
optimization approach in which the Disco problem is modeled as
the upper-level problem and the MGs problems and day-ahead
wholesale market clearing process are modeled as the lower-level
problems. To include the uncertainty of renewable energy
sources, a risk-based two-stage stochastic problem is formulated,
in which the Disco’s risk aversion is modeled by using the
conditional value at risk. The resulting non-linear bi-level model
is transformed into a linear single-level one by applying the
Karush-Kuhn-Tucker conditions and the duality theory. The
effectiveness of the model is shown in the application to the IEEE
33-bus distribution network connected to the IEEE RTS 24-bus
power system.

... The optimal operation problem of a distribution company (DisCo) considering real-time and day-ahead electricity markets is modeled using two-stage deterministic and stochastic optimization approaches in [2] and [3], respectively. The operation of a DisCo in the presence of DERs is modeled in [4][5][6][7] when it participates as a price-taker or price-maker player in the energy markets. ...

... The operation of a distribution network considering a different charging strategy of electric vehicles is modeled using a stochastic optimization approach in [9]. In [2][3][4][5][6][7][8][9], the operation of the ADNs is modeled considering the DisCo as the only decision maker. ...

Implementation of distributed energy resources (DERs) has led to a decrement in the cost of supplying
demand in distribution networks. Integration of DERs in the forms of micro-grids (MGs) is a solution
to enhance the operation of these resources in the low voltage networks. To meet the demand by
MG operator, both technical and economic characteristics as well as the prices offered by retailers
are considered to schedule DERs optimally. In these networks, the profit of retailers is maximized
by power trading with MGs and optimally purchasing the energy from wholesale markets. Due
to the existence of several retailers and MGs in active distribution networks (ADNs), hierarchical
decision-making frameworks are needed to model their operation problem. For this purpose, a bi-level
optimization technique is proposed in this paper to model the operation problem of retailers and MGs
as decision-making variables in distribution networks in the upper and lower levels, respectively. To
solve the proposed model, multi-objective particle swarm optimization (MOPSO) algorithm is used.
The proposed model and its solution method are applied to a hypothetical distribution network with
several retailers and MGs to validate the theories and discussions. Numerical results show that the
maximum capacity of DG and the amount of demand have an important effect on this decision and
the prices of purchased power from wholesale markets determine the amount of retailers’ offers to
MGs.

... The authors of [12] introduce a model for operation of a Disco in the presence of DER aggregators. A two-stage optimization approach is introduced in [17,18] to formulate the behavior of a Disco, as a price-taker player, in the DA and RT markets. ...

... A stochastic approach to derive optimal bidding strategies for a wind power producer and energy storage in the Spanish multi-stage market, consisting of DA, intraday, and RT markets, is proposed in [20]. Generally stated, in [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], Disco is considered as a price-taker player in the DA and RT markets. ...

In Active Distribution Networks (ADNs), Distribution Company (Disco) follows two main
strategies of dispatching of Distributed Energy Resources (DERs) and trading energy with
wholesale energy markets, including Day-Ahead (DA) and Real-Time (RT) markets, to meet the
demand. An attempt is made in this paper to model the strategic behavior of the Disco, in the
wholesale DA and RT energy markets, through a bi-level optimization approach. While the
objective of the upper-level problem is to minimize the expected cost of the Disco, the lower-level
problem (with two optimization problems) formulates to simultaneously maximize the socialwelfare
of the DA market and minimize the cost of the RT market. Furthermore, uncertain behavior
of renewable energy sources as well as demand is tackled into the problem formulation. To this
end, Disco decision-making represents as a risk-based two-stage stochastic problem where the
Disco’s risk aversion is modeled using conditional value at risk (CVaR) method. Generally stated,
the proposed model is a non-linear bi-level problem which may be transformed into a non-linear
but single-level problem through Karush-Kuhn-Tucker (KKT) conditions and dual theory. Detailed
numerical results on a 6-bus and RTS 24-bus power systems are used to demonstrate efficiency of
the proposed model. Moreover, sensitivity analysis is carried out to investigate the effect of riskaversion
parameter on the decision making of the Disco and the offers/bids in

... In this context, the operation problem of the Disco as a price-taker player has been studied in many works. An appropriate decision making frameworks is presented for the operation problem of the Disco in day-ahead and real-time electricity markets in [1,2]. The operation problem of Disco in day-ahead energy market is modeled considering its optimal behavior to trade energy with plug-in electric vehicles in [3]. ...

... The operation problem of Disco in wholesale energy and reserve markets is modeled considering its optimal behavior with microgrids in its network in [9][10][11]. As stated before, in all these papers, the Disco is modeled as a price-taker decision maker in wholesale markets [1][2][3][4][5][6][7][8][9][10][11]. On the contrary, in [12] the Disco is considered as a price-maker player in wholesale energy markets while it exchanges energy with DER owners. ...

The decision making framework in power system has changed due to presence of distributed energy resources (DERs). These resources are installed in distribution networks to meet demand locally. Therefore, distribution companies (Discos) are enabled to supply energy through these resources to meet their demand at a minimum operation cost. Moreover, in this framework, the Disco will change its role in the wholesale energy market from price taker to price maker as a decision maker. Moreover, DERs can serve reserve in their normal operation. This opportunity facilitates Disco to provide reserve to the wholesale reserve market. The strategic behavior of a Disco in wholesale energy and reserve markets is modeled in this paper as a bi-level optimization problem. The operation problem of the Disco and the Independent System Operator (ISO) are modeled in the upper-and lower-level problems, respectively. Karush-Kuhn-Tucker (KKT) dualilty theory conditions are employed to transform the proposed nonlinear bi-level problem to a linear single level one. Numerical studies demonstrate the proficiency of the proposed model and the solution methodology. Index Terms-bi-level optimization problem, Disco operation problem, energy and reserve markets.

... • Constraints of trading energy and reserves with the Disco: [30,31] Table 3 The uncertain parameters modeled in DNOP in the presence of DERs. ...

... Deterministic Stochastic Solar radiation Wind speed Demand Electricity price [30,31] ...

The distribution network operation problem (DNOP) is an optimization problem in which the objective function is the total operation cost of the distribution company (Disco), to be minimized considering the technical constraints of the network. In the presence of distributed energy resources (DERs) and microgrids (MGs), new decision makers, including MG and DER operators or managing entities, are emerging and are changing the decision-making framework for distribution systems. To describe the cooperation and competition between the Disco, MG and DER operators, different frameworks and models have been proposed in the literature. Moreover, different computational techniques and metaheuristic algorithms have been used to solve the optimal operation problems. Hence, this paper considers DNOP as one of the timely problems under study and of major interest for future research, presenting a comprehensive review on the decision-making frameworks referring to DNOP in the presence of DERs and MGs, as a new contribution to earlier studies. The focus is set on the comparison among different frameworks characterized by increasingly higher level of participation of the DER managers to the distribution system operation, offering a complementary view with respect to available reviews on similar topics based on technical aspects of the DER connection and integration in MGs and distribution networks, which is noteworthy

... The decision-making problem of a Disco that participates as a price-taker in the electricity market is modeled in different ways. Two-stage deterministic and stochastic optimization approaches are proposed in both DAEM and real-time energy market (RTEM) in [7] and [8], respectively. Optimal scheduling of DERs by the Disco is done regarding the forecast prices of DAEM and the reserve markets in [9]. ...

... (7)- (10). The amount of feeders current is determined by (7). The upper and lower limitations of current and voltage of the network are modeled by (8). ...

One of the emergent prospects for active distribution networks is to establish new roles to the distribution company (Disco). The Disco can act as an aggregator of the resources existing in the distribution network, also when parts of the network are structured and managed as microgrids (MGs). The new roles of the Disco may open the participation of the Disco as a player trading energy in the wholesale markets, as well as in local energy markets. In this paper, the decision making aspects involving the Disco are addressed by proposing a bi-level optimization approach in which the Disco problem is modeled as the upper-level problem and the MGs problems and day-ahead wholesale market clearing process are modeled as the lower-level problems. To include the uncertainty of renewable energy sources, a risk-based two-stage stochastic problem is formulated, in which the Disco’s risk aversion is modeled by using the conditional value at risk. The resulting non-linear bi-level model is transformed into a linear single-level one by applying the Karush-Kuhn-Tucker conditions and the duality theory. The effectiveness of the model is shown in the application to the IEEE 33-bus distribution network connected to the IEEE RTS 24-bus power system.

... In [12], an online energy management system based on Lyapunov optimization has been proposed. In [13], a two-stage hierarchical model which is day-ahead operation model and real-time operation model for the distribution companies (Disco's) has been implemented. It has formulated to maximize the Disco's revenue, by reducing its short-term costs. ...

... The active and reactive power equations of the network are given in (11) and (12). The voltage at the bus should be with in the minimum (V min ) and maximum (V max ) limits (13). Flow limits is a major factor since BESS is placed in the network, BESS require proper control of charge and discharge rates to maintain the flow in specified limits P max from bus i to bus j i.e., P ij . ...

... The reactive power is calculated as [15], [21], [22]: ...

... At the same time, the charging/discharging status command is send to BESS. Real-time scheduling horizon considered as one hour [18], [22]. ...

This paper develops a detailed and sequential procedure for short term operation of an aggregator to minimize the cost of the consumer and risk of the aggregator, which includes day-ahead (DA) and real-time (RT) operation. DA operation has two stages: consumer load scheduling and risk-based energy procurement. Consumer load scheduling implemented over a radial distribution network by a cost minimization objective function which considers electricity price and solar PV uncertainty, peak-to-average ratio, and phase unbalance. This model is formulated as a mixed integer non-linear program. In the second stage, a risk based energy procurement is formulated, here, the aggregator has choice of energy procurement from wholesale market either in DA market or RT market to meet the scheduled power. In RT operation, the aggregator take a decision on share of RT purchases and battery schedules to minimize the cost of scheduled load power deviations. In order to realize this, a rolling window optimization is implemented which is modeled as mixed integer problem. The framework is demonstrated with a detailed case study of 15 node radial active distribution network consisting of 420 number of residential customers.

... In order to limit the aggregator's monopolistic profitability, an adequate competition in retail markets is necessary (Momber et al., 2016). Additionally, traditional problems like congestion, voltage and frequency derivations, and line losses of a power sector including buildings can be improved using distributed generation sources and inter-ties, which add complexity from both technical and economic perspectives, and hence, the emergence of new entities within the distribution network domain, namely, retailers, is sensed (Algarni & Bhattacharya, 2009;Teimourzadeh et al., 2021). ...

Nowadays, the world is facing energy crisis and environmental issues. This is why the energy demand is increasing in different energy sections. The buildings as a large energy consumer are critical to face with these issues. To overcome these challenges, the conventional active buildings are moving toward the active building. Demand flexibility and self-generation are two characteristics of the active building. However, the main feature of such emerging buildings is related to their flexibility demand. Demand flexibility in active buildings is enabled by demand response programs. The change in energy consumption pattern by residents of buildings is the aim of implementing such programs. Demand response programs are designed and managed by aggregators in retail markets. In addition, the enabling technologies for implementing such programs are provided by aggregators. Therefore, the aggregators are important for developing acting buildings. Accordingly, in this chapter, the role of aggregators in demand flexibility of active buildings is outlined. Firstly, the concept of aggregators and retail electricity markets are presented. Then, the benefits, barriers, motivators, and challenges of demand response programs are discussed. Moreover, the existing demand response programs and enabling technologies for implementing such programs are described in this chapter.KeywordsActive buildingsAggregatorRetailerDemand responseDemand-side responseRetail electricity marketEnabling technologies for demand responsePrice-based demand responseIncentive-based demand responseEnergy managementEnergy efficiencyEnergy savingDemand flexibilitySmart buildingsDemand response challengesDemand response barriersDemand response drivers

... Nodal pricing is one of the financial reward methods used by DISCO to manage privately owned EG units and to enable EG owners to take technical decisions [21]. LMP is the most effective approach for evaluating the nodal price in practice [5,15]. ...

In this study, a Nucleolus Theory (NT) based iterative method has been presented to compute Distribution Locational Marginal Price (DLMP) at buses where embedded generator (EG) units were installed in the distribution network. The NTbased iterative method provides financial incentives to EG owners as per their contribution to loss reduction and emission reduction for specified loading conditions. In this study, DLMP values depend on the decision-maker preference among loss reduction, emission reduction, and distribution company additional benefit. The main objective of the proposed method is to optimize the active power loss and emissions based on DISCO decision-maker priority subjected to EG capacity. DLMP value for each EG unit is a variable that has to compute based on DISCO decision-maker priority. This proposed NT based iterative method has been implemented on Taiwan power company Distribution System (TPCDS) consisting of 84 buses with 15 EG units, and 201 bus radial distribution networks with 15 EG units under MATLAB environment and computed DLMP values based on the contribution of EG units on loss and emission reduction. It was inferred from the results that the proposed method enables EG owners to receive more financial benefit and also enables DISCO decision-makers can operate the network with more optimal in terms of loss and emissions.

... In [4], a bi-level method of retail pricing is presented based on indirect load control, where the decision-making at one level affects the decision space of the other level [5]. In [6] and [7], a two-stage hierarchical framework is provided for distribution company (DISCO) operators in day-ahead and real-time electricity markets considering the uncertainties of prices in the electricity markets and load demand. Moreover, the participation of DISCO operators in energy and reserve markets is modeled in [8] and [9]. ...

This paper presents a risk-based competitive bi-level framework for optimal decision-making in energy sales by a distribution company (DISCO) in an active distribution network (ADN). At the upper level of this framework, the DISCO and a rival retailer compete for selling energy. The DISCO intends to maximize its profit in the competitive market. Therefore., it is very important for the DISCO to make a decision and offer an optimal price for attracting customers and winning the competition. Networked microgrids (MGs) at the lower level, as the costumers, intend to purchase energy from less expensive sources in order to minimize costs. There is a bi-level framework with two different targets. The genetic algorithm is used to solve this problem. The DISCO needs to be cautious, so it uses the conditional value at risk (CVaR) to reduce the risk and increase the probability of making the desired profit. The effect of this index on the trade between the two levels is studied. The simulation results show that the proposed method can reduce the cost of MGs as the costumers, and can enable the DISCO as the seller to win the competition with its rivals.

... The operation problem of distribution networks in the presence of DERs and MGs has been investigated from different viewpoints in the literature. In Ref. [5], a twostage hierarchical framework is utilized to model the decision making problem of a Disco in wholesale energy markets. In Ref. [6], the previous study is extended considering the uncertainties of real-time electricity prices and demands. ...

Active distribution grids (ADGs) consist of several distributed generations (DGs) and controllable loads (CLs). These resources are utilized in the form of several microgrids (MGs) which in turn facilitate managing of ADGs. Therefore, the problem of distribution company (DISCO) and MGs operation requires a hierarchical decision-making framework. An attempt is made in this paper to model such framework as a bi-level optimization problem. In the proposed bi-level model, the objective of the upper level (leader) problem is to maximize the profit of DISCO, and the objective of the lower level (follower) problems is to minimize the cost of MGs. The resulting model is a nonlinear bi-level problem which is transformed into a linear single-level problem through Karush-Kuhn-Tucker (KKT) conditions and dual theory. Since the proposed model creates a retail electricity market in distribution grid, two frameworks are considered for this market: various and uniform retail electricity prices. To illustrate the effectiveness of the model, a hypothetical distribution grid is considered as the case study. The impacts of the market price and various demand levels of MGs on the results are investigated in two scenarios.

... The profit model of the DisCo targets on determining the optimal planning of distribution lines in a radial distribution system and signing DR capacities with electricity retailers [40][41]. ...

Competition on distributed generation (DG) investments among multiple stakeholders in a distribution system results in incompleteness of market information, in which each stakeholder does not have full knowledge on investment and operation decisions of other participants. It leads to an incomplete information game among multiple stakeholders. This paper discusses a multi-lateral incomplete information game based approach to study distribution system planning while considering both supply and demand sides competitions. Profit models of three types of stakeholders, including DG investors (i.e., DG units are investor-owned), electricity consumers, and the distribution company, are constructed. The interaction among the stakeholders and their gaming behavior are further studied under the context of multi-lateral incomplete information. Bayesian Nash equilibrium form of the multi-lateral incomplete information game is obtained via Harsanyi transformation. An improved co-evolutionary algorithm is adopted to find the Bayesian Nash equilibrium. Simulation results on a modified IEEE 33-bus test system show that, compared with the complete information game method, the proposed approach presents higher expected profits and more accurate planning schemes. Indeed, the proposed approach reflects the realistic planning process of distribution systems under a deregulated competitive environment, and it ensures fairness of competition among self-interested independent stakeholders while guaranteeing their individual performance.

... The profit model of the DisCo targets on determining the optimal planning of distribution lines in a radial distribution system and signing DR capacities with electricity retailers [32], [33]. ...

... Tais mercados são caracterizados pela alta volatilidade dos preços, podendo levar a riscos de perdas monetárias. Portanto, é importante que a maioria das transações seja realizada no mercado DA para que os riscos associados às transações no mercado RT sejam minimizados (ALGARNI;BHATTACHARYA, 2009a; WANG et al., no prelo).No Brasil, ao contrário do que ocorre em outros países, não há ainda um mercado RT que, de fato, cumpra as funções de eficiência relacionadas a um mercado de tempo real. Opreço de liquidação das diferenças (PLD) é o preço de curto prazo no Brasil, pelo qual são liquidadas as diferenças entre energia contratada e energia gerada. ...

In this work, we propose a solution solution procedure for the short-term operation planning of distribution systems with distributed generation (DG) considering a probabilistic approach. A sequential formulation based on the distribution company's (DisCo's) perspective is presented. The DisCo’s operational decisions are optimized first in a day-ahead (DA) operation stage, and then in real-time (RT). The DA operation maximizes the difference between the energy sold to customers and the purchases from the wholesale electricity market and distributed generators. In RT, the objective is to minimize the adjustments that are required to accommodate deviations from forecasted quantities. The voltage-sensitiveness of power load injections and demand related constraints are explicitly formulated. The network is modeled using full ac power flow equations. In addition, a nodal-based reactive power pricing mechanism is proposed to be incorporated in the formulation. The resulting models are characterized as large-scale non-linear non-convex mathematical programs with continuous and discrete variables. A pseudo-dynamic Tabu Search (TS)-based solution algorithm is used to tackle the problem in an effective manner, without linearizations. Numerical results from the 69-bus and 135-bus distribution test feeders illustrate the performance of the proposed approach.

... In a deregulated electricity market, DISCOs' decisions are made to improve the both of economic and technical aspects of the network [1,2]. Moreover, with the technical developments in the distribution network components such as DGs on the one hand, and the development of retail competition in the electric power industry on the other hand, the role of DISCOs has become more vital for the efficient operation of DGs in the distribution networks [3]. ...

... Technical constraints including power balance constraint, DG unit constraints, energy storage constraints, and LC limits are presented hereinafter [31], [32]. ...

... In deterministic method, all input parameters of a specific time period are known and the outputs are determined for that period [10,11]. The authors in Ref. [12] provided a deterministic scheme for day-ahead management of a distribution company in electricity market that works in a competitive environment. In Ref. [13], an economic performance model for a microgrid is proposed, which aims to employ wind turbines and solar panels in the scheduling of the studied network utilizing fuzzy method. ...

Uncertainties of load demand and power output of renewable-based energy sources as well as participation of responsive loads in energy supply can be identified as the main issues of the future power networks. Accordingly, it is essential to develop practical approaches for dealing with the uncertainties of wind power and load in optimal scheduling of such systems. This paper proposes a new uncertainty-modeling approach based on Hong's two-point estimate method (T-PEM) for optimal day-ahead scheduling (ODAS) of a smart distribution system (SDS). The proposed method seeks to minimize the functional cost of energy and reserve requirements of SDS in the presence of wind turbines, diesel generators and battery energy storage system considering uncertainties of wind production and load demand. Also, according to importance of enabling consumers to contribute in energy and reserve supply of SDSs, the present work studies the implementation of two various demand response (DR) programs in energy and reserve management of a SDS. The proposed method is applied on IEEE 33-bus distribution test system to investigate the efficiency and performance of the proposed model, which confirms the validity and practicality of the presented model.

... In [13], uncertain variables related to SDS operation are modeled by PDF, and the operation is accomplished based on probabilistic scenarios. In [14], the model presented in [7] has been developed so that the expected cost of network performance is minimized and the risk associated with the uncertainties in the problem is considered in this study. However, the stochastic model presented in this reference investigates energy planning without paying attention on RESs and the risk associated with their uncertainties. ...

The distribution system operator (DSO) needs an optimal day-ahead scheduling (ODAS) for the economic and sustainable supply of electrical energy considering input parameters such as the price of the upstream grid. Next-generation distribution networks or smart distribution networks are the future networks where responsive loads are available. Renewable sources include wind turbines and solar panels have expanded. The presence of electric vehicles and the purchase from the electricity upstream market is provided, and the network can be programmed and controlled through the devices which record and transmit information. In this chapter, a robust optimization (RO) method has been proposed to minimize the cost of ODAS of smart distribution system (SDS) considering load-responsive and renewable energy sources (RESs) such as wind turbine (WT) and nonrenewable sources such as diesel generators (DGs) and battery energy storage system (BESS). The proposed method considers all the technical constraints of the upstream grid and DGs and the utilized BESS. In order to model SDSs, a 33-base IEEE test system has been used in the evaluation of the proposed model. The proposed ODAS concept determines the optimal level of exchange with the upstream network, the production of each dispersed generation unit, and the participation of demand response (DR) programs. It also provides an optimal layout for charging and discharging the BESS. It can be observed that the proposed model has the optimal scheduling capabilities of the SDSs considering the uncertainties of power market price. Moreover, it is observed that the resilient optimization method reduces the cost of network operation and confronts the price of electricity with uncertainty.

... In [17], the authors established a model of power retailers with distributed generation. A two-stage hierarchical model was used to simulate the day-ahead market and real-time market. ...

Due to market price uncertainty and volatility, electricity sales companies today are facing greater risks in regard to the day-ahead market and the real-time market. Along with introducing the Time of Use (TOU) price for the customer as a type of balancing resource to avoid market risk, electricity sales companies should adopt the market risk-aversion method to reduce the high cost of ancillary services in the real-time market by using multi-level market transactions, as well as to provide a reference for the profits of power companies. In this paper, we establish a non-linear mathematical model based on stochastic programming by using conditional value-at-risk (CVaR) to measure transaction strategy risk. For the market price and consumer electricity load as the uncertain factors of multi-level market transactions of electricity sales companies, the optimal objective was to maximize the revenue of electricity sales companies and minimize the peak-valley differences in the system, which is solved by using mixed-integer linear programming (MILP). Finally, we provide an example to analyze the effect of the fluctuation degree of customer load and market price on the profit of electricity sales companies under different confidence coefficients.

... A lot of studies have been focused on the optimal operation of DISCO and MGs considering economic and technical issues. In [1], a two-level decision making model for a DISCO in day-ahead electricity market is proposed considering interruptible loads (ILs) and distribution generations (DGs). In [2], a stochastic framework for short-term operation of DISCO is presented considering day-ahead and real-time markets. ...

... A lot of studies have been focused on the optimal operation of DISCO and MGs considering economic and technical issues. In [1], a two-level decision making model for a DISCO in day-ahead electricity market is proposed considering interruptible loads (ILs) and distribution generations (DGs). In [2], a stochastic framework for short-term operation of DISCO is presented considering day-ahead and real-time markets. ...

With the integration of microgrids (MGs) in future distribution networks (DNs), it is essential to develop a practical model for the distribution company (DISCO). Optimal operation of MGs is not generally consistent with DISCO, especially when they are operated by private owners. To this end, a decentralized robust model for optimal operation of DISCO with private MGs (PMGs) is proposed in this paper. The objective is to minimize the total operation cost of the system including DN and PMGs through coordinated operation of them. The enforced operational uncertainties are handled using an adaptive robust optimization (ARO) approach, enabling the operators of DISCO and PMGs to adjust different conservation levels during operating horizon. To respect the ownership of PMGs, a decentralized algorithm based on alternating direction method of multipliers (ADMM) is proposed to efficiently solve the resulting ARO model in which the operating problems of DISCO and PMGs are optimized independently. Case studies of a test system including modified IEEE 33-bus distribution network with three PMGs is used to demonstrate the effectiveness of the proposed model.

... The power industry has encouraged distributed generations (DGs) in increasing power production due to the potential advantage of these resources to both the DG owner and the power market as well. These advantages include economic profits, voltage profile modification, network loss reduction, and deferral of the distribution network expansion [1,2]. There are various definitions for DG, but it is generally defined as the electricity production by small-scale units situated in the distribution networks or near the consumers [3]. ...

Virtual power plant (VPP) is an entity that aggregates generation of distributed generation units. Thus, it is important to design a competitive framework which models the participation of the VPPs in the electricity market along with their trading with distribution company (DisCo). This study proposes a bilevel programming framework using the concept of multi-leader–follower game theory to determine the optimal contract prices of VPPs which compete against each other in the distribution network. The optimal prices are used for setting annual bilateral contracts with VPPs. The leader layer of the proposed bilevel problem includes VPPs, which try to maximise their profits, while the follower problem corresponds to the cost function of the DisCo, which aims to minimise the payments of supplying the forecasted demand. The DisCo optimisation problem is transferred by its Karush–Kuhn–Tucker optimality conditions, turning each VPP problem into an equivalent single-level optimisation problem. Some case studies are defined and implemented to assess the performance of the proposed scheduling model.

... The diesel units as dispatchable units may be used to lower the cost. The day ahead power at each hour is obtained by minimizing the cost of mentioned components [23]: ...

With increase of renewable energy penetration, mitigating the power fluctuations in real-time demands a higher spinning reserve. Distribution System Company (DISCO) performs an optimization problem for the next day with the predicted load. In real time, due to fluctuations of renewables, DISCO uses DR to counteract the fluctuations of renewables. DR is provided by parties called demand response aggregators (DRA) which collect the DR powers from residential and commercial buildings and offer them to DISCO. The aggregators are competing to sell the amount of demand response to DISCO for next 5 or 15 minutes ahead. The competition is modeled with incomplete information Bayesian game theory. Best response of each aggregator is derived and the aggregate power of DRAs are offered to DISCO. DISCO performs an optimization to meet the load demand in real-time market with demand response contributing to remove the unbalance between load and power procurement in real time. By including the offset of demand curve, DISCO can optimize the amount of power purchased from DRAs. Day ahead as well as real-time optimizations of DISCO have been analyzed in this paper and contribution of DR as well as other factors have been presented.

Renewable generation as well as electrical demand uncertainties cause significant technical challenges in addition to associated financial consequences in smart distribution networks (SDNs), particularly in the electricity markets, which are restructured and are featured by smart grids. In this paper, a risk-averse-strategy-based decision making tool is proposed to help the smart distribution network operator (SDNO) in day-ahead operational practices including optimal unit commitment (UC) and optimal distribution feeder reconfiguration (DFR). The proposed tool aims to reduce the consumers’ electricity prices as well as to optimize the financial transactions with the energy market, reliability of distributed generation (DG), electricity storage system (ESS) dispatch, and planning interruptible electrical demands in order to secure the specified revenue targets for SDNO by means of the risk-averse strategy. A bi-level stochastic optimization problem based on information gap decision theory (IGDT) is considered to preserve the SDNO from the risks of information gap between the predicted and actual uncertainty variables. The bi-level stochastic optimization problem is applied to a single-level problem obtained by Karush–Kuhn–Tucker method. As uncertainty variables compete to expand their enveloped-bounds, the enhanced ε -constraint method is employed to address the multifaceted IGDT-based stochastic optimization problem proposed in the study. Finally, the efficiency and efficacy of the proposed model are evaluated on an IEEE 33-bus SDN.

Distribution networks are envisaged to host significant number of electric vehicles and potentially many charging stations in the future to provide charging as well as vehicle-2-grid services to the electric vehicle owners. The main goal of this study is to develop a comprehensive day-ahead scheduling framework to achieve an economically rewarding operation for the ecosystem of electric vehicles, charging stations and retailers using a comprehensive optimal charging/discharging strategy that accounts for the network constraints. To do so, an equilibrium problem is solved using a three-layer iterative optimisation problem for all stakeholders in the ecosystem. EV routing problem is solved based on a cost-benefit analysis rather than choosing the shortest route. The proposed method can be implemented as a cloud scheduling system that is operated by a non-profit entity, e.g., distribution system operators or distribution network service providers, whose role is to collect required information from all agents, perform the day-ahead scheduling, and ultimately communicate the results to relevant stakeholders. To evaluate the effectiveness of the proposed framework, a simulation study, including three retailers, one aggregator, nine charging stations and 600 electric vehicles, is designed based on real data from San Francisco, the USA. The simulation results show that the total cost of electric vehicles decreased by 17.6%, and the total revenue of charging stations and retailers increased by 21.1% and 22.6%, respectively, in comparison with a base case strategy.

The rapid development of distributed energy resources and active distribution networks has resulted in increased interest in the concept of a distribution locational marginal price (DLMP). In this paper, first, an extension of a previous DLMP model based on linearized power flow - distribution (LPF-D) is applied to provide an explicit formulation of the voltage cost component (VCC), together with the energy cost component (ECC) and the loss cost component (LCC). Then, this DLMP model is extended to include a three-phase distribution model to form the proposed three-phase DLMP (TDLMP) based on the three-phase LPF-D (TLPF-D). An imbalance cost component (ICC) is included in this three-phase DLMP model. Finally, the proposed TDLMP model is applied to various case studies to demonstrate the benefit of distributed energy resources (DERs), including distributed generation (DG) and demand response (DR). The case studies verify that the VCC and the ICC are significant components of the TDLMP in low-voltage and unbalanced distribution networks. In addition, DERs can significantly reduce the TDLMP, especially the VCC and ICC, by improving the voltages and reducing phase imbalance. Thus, the proposed TDLMP provides a quantitative framework to evaluate the economic benefits of DERs in competitive market-based distribution operations.

Uncertainties involved in renewable generation and electrical demand pose significant technical challenges with the concomitant financial consequences in smart distribution networks (SDNs), particularly in the current electricity market, which is restructured and features smart grids. The present paper introduces a decision‐making tool based on a risk‐averse strategy to help with the smart distribution network operator (SDNO) in day‐ahead operational practices, including optimal unit commitment (UC) and optimal distribution feeder reconfiguration (DFR). The tool is meant to reduce electricity prices presented to the electrical consumers and to optimize financial transactions with the energy market, distributed generation (DG) reliability, electricity storage system (ESS) dispatch, and planning interruptible electrical demands to secure specified revenue targets for SDNO with the risk‐averse strategy. A bi‐level stochastic optimization problem based on Information gap decision theory (IGDT) is considered to keep the SDNO from risks inherent in the information gap present between the predicted and actual uncertainty variables. The bi‐level stochastic optimization problem is reorganized into a single‐level problem obtained by Karush‐Kuhn‐Tucker method. As uncertainty variables compete to expand their enveloped‐bounds, multi‐objective covariance matrix adaptation‐evolution strategy (MOCMA‐ES) is employed to address the multifaceted IGDT‐based stochastic optimization problem proposed in the study. Finally, the efficiency and efficacy of the suggested model are appraised on an IEEE 33‐bus SDN. Simulation results show that optimal UC with both DFR and demand response program increases the total revenue by 8.1% compared to optimal operation without them.

Implementation of distributed energy resources (DERs) has led to a decrement in the cost of supplying demand in distribution networks. Integration of DERs in the forms of micro-grids (MGs) is a solution to enhance the operation of these resources in the low voltage networks. To meet the demand by MG operator, both technical and economic characteristics as well as the prices offered by retailers are considered to schedule DERs optimally. In these networks, the profit of retailers is maximized by power trading with MGs and optimally purchasing the energy from wholesale markets. Due to the existence of several retailers and MGs in active distribution networks (ADNs), hierarchical decision-making frameworks are needed to model their operation problem. For this purpose, a bi-level optimization technique is proposed in this paper to model the operation problem of retailers and MGs as decision-making variables in distribution networks in the upper and lower levels, respectively. To solve the proposed model, multi-objective particle swarm optimization (MOPSO) algorithm is used. The proposed model and its solution method are applied to a hypothetical distribution network with several retailers and MGs to validate the theories and discussions. Numerical results show that the maximum capacity of DG and the amount of demand have an important effect on this decision and the prices of purchased power from wholesale markets determine the amount of retailers’ offers to MGs.

This article presents a game-theoretic method for operation of a smart distribution grid equipped with distributed generation and demand response programs. The problem is formulated as a noncooperative game to investigate the impacts of distributed power supply and autonomous demand response in comparison to a centralized method. The main idea and key contribution of this article is the provision of a mathematically provable game theory-based method for the cooperation of distributed generation units and smart customers in a near-real distribution system considering the existing issues related to the operational limitations of the real power grids. To verify the proposed method, several case studies have been carried out and compared. The results show that the proposed method is effective in reducing the total cost and total power losses, and can also improve grid performance in terms of reactive power support, voltage profile, and load profile flattening.

Electricity retailer using demand response (DR) programs can reduce their cost in procuring consumers energy. In this chapter, several new demand response schemes are proposed to reduce retailer cost. These new schemes include pool-order DR, forward DR, and reward-base DR. Information gap decision theory (IGDT) technique is proposed to handle the pool market price uncertainty. Furthermore, optimal bidding strategy of electricity retailer is obtained using IGDT technique based on opportunity and robustness functions. Optimal bidding strategy provides stepwise power price in the power price uncertainty condition for submiting to day-ahead market in order to purchase power from pool market. The proposed model based on IGDT technique can be solved using standard Branch and Bound (SBB) solver under GAMS software.

Because of the deregulation of electric power, electricity supply has changed from an exclusive supply by general electric utilities in each region to a competitive supply by various electric utilities. Furthermore, the wholesale electric power exchange (EPX) has been established, and it is becoming possible to procure and sell electricity through the market. Because the prices in the EPX are uncertain, a planning method considering the uncertainty is needed. A community energy management system (CEMS) stands between energy consumers in the community and energy suppliers. A CEMS purchases energy from external power supply sources and operates the equipment to provide ancillary service to consumers. In this paper, we consider the day‐ahead scheduling problem of a CEMS group using stochastic optimization. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Retail pricing can be well deployed with support of distribution companies (DISCOs) to promote demand response (DR) programs using heating, ventilating, and air-conditioning (HVAC) systems as thermal energy storage resources. This paper proposes a retail pricing strategy to assist a DISCO and end-users in achieving optimal price-based DR for HVAC systems, while considering operational aspects of distribution network (DN). The pricing strategy is developed using a bi-level decision model. In the upper level, the DISCO optimizes the retail price to maximize its profit, and in the lower level, the end-users schedule HVAC loads to minimize their electricity bills. The nonlinear thermal response model of an experimental building room is integrated with a data-driven model of an HVAC system and incorporated into the pricing strategy using piecewise linear approximation. The proposed strategy also employs time-of-use (TOU) rates as retail price caps, leading to low-level price volatility. Case studies are presented for various ranges of bus voltage deviation and penetration levels of HVAC systems. The results demonstrate that the proposed strategy is effective for determining the optimal balancing point between the DISCO’s profit and end-users’ electricity bills while enabling the DISCO to reliably regulate the network voltages via the price-sensitive HVAC units. IEEE

In this paper, robust optimization approach is proposed to handle market price uncertainty in which the upper deviation from forecasted value of pool price will be considered for risk analysis for a retailer. Objective of this paper is minimization of energy procurement cost for retailer from pool market, forward contracts and demand response programs (DRP). Therefore, three new designs of demand response (DR) programs have been proposed in this study which retailer can use to procure their required energy. These new DR schemes consist of pool-order option, forward-DR and reward-based DR contracts. The robust optimization approach examines the retailer’s performance at risk-averse and risk-neutral strategies, in which risk-neutral explains the normal performance of retailer, and risk-averse explains the risky performance of retailer. The proposed robust scheduling of retailer is modeled via MIP model which can be solved using CPLEX solver under GAMS software. The achieved results show that the retailer cost in risk-neutral strategy is reduced due to use of new DR schemes. Also, in risk-averse strategy, retailer cost reduction is more than the risk-neutral strategy use of new DR schemes.

In this paper, a proportional nucleolus game theory (PNGT)–based iterative method has been presented to compute locational marginal price (LMP) at buses where distributed generation (DG) units were installed in a distribution network. Proportional nucleolus theory is one of the solution concepts for a cooperative game theory problem. The PNGT‐based iterative method provides financial incentives in terms of LMP to DG owners as per their contribution in loss reduction and emission reduction at a particular loading on the distribution system. In this method, LMP values depend on the distribution company's decision maker's preference among loss reduction, emission reduction, and distribution company's additional benefit. This proposed PNGT‐based iterative method has been implemented on a Taiwan power company's distribution network consisting of 84 buses with 15 DG units using MATLAB. The computed LMP values have operated the network based on decision‐maker priority so as to enable fair competition among DG owners.

This work presents a probabilistic sequential framework for short-term operation of distribution companies (DisCos)
participating in the day-ahead (DA) and real-time (RT) markets. In the proposed framework, the DisCo's operating decisions are
sequentially optimised; first, in a DA operation stage, and then in RT. The DA decisions are driven by the DisCo's profit
maximisation, while the DisCo aims to minimise the actions required to accommodate deviations from forecasted quantities (i.e.
the DA decisions) in the RT operation stage. This sequential approach considers realistic voltage-sensitive loads and full ac
power flow equations to represent the realistic network's active and reactive power injections. In addition, the operation of
stationary batteries and the demand elasticity under time-varying retail prices are explicitly modelled. The two resulting models
are large-scale highly non-linear non-convex mathematical problems with continuous and discrete variables. A pseudo-dynamic
tabu-search-based solution algorithm is used as an alternative to conventional optimisation solvers in order to tackle the
problem in an effective manner, without linearisations. Numerical results from 69- and 135-bus distribution systems illustrate the
performance and the scalability of the proposed approach.

This paper presents a novel simulation system to analyze adaptive behaviors of agents in a deregulated electricity retail market. We develop a learning framework which enables the agents to autonomously acquire the action rules. In this paper, the XCS (extended classifier system) is employed as a learning algorithm of the agents. XCS can efficiently generate action rules in the dynamic environment such as the deregulated retail market affected by the interaction among many agents. The artificial retail market shows complicated behavior by the interaction among the agents, and therefore the agent-based simulation can provide some technical findings due to the interactions among autonomous agents which are not always rational in a sense of optimal behaviors. We provide new insights based on the behavior analysis of the agents in the artificial retail market simulation.

High penetration of distributed generation (DG) resources is increasingly observed worldwide. The evolution of this process in each country highly depends on the cost of traditional technologies, market design, and promotion programs and subsidies. Nevertheless, as this trend accelerates, higher levels of penetration will be achieved and, in turn, a competitive market integration of DG will be needed for an adequate development of the power sector. This paper proposes a competitive market integration mechanism for DG in a pool-based system. The mechanism encompasses both energy and capacity payment procedures in the wholesale market with DG units located at the distribution level. The proposed model is validated for the current Chilean regulation framework and extended to more general market structures. The model can be considered a novel development on the design of competitive markets for DG resources, which are still dominated by subsidies/compensation schemes.

This work presents a novel day-ahead energy acquisition model for a distribution company (DisCo) in a competitive market based on Pool and financial bilateral contracts. The market structure encompasses wholesale generation companies, distributed generation (DG) units of independent producers, DG units owned by the DisCo, and load curtailment options. Thus, while satisfying its technical constraints, the DisCo purchases active and reactive power according to the offers of DG units, customers, and the wholesale market. The resulting optimal power flow model is implemented with an object-oriented approach, which is solved numerically by making use of a branch and border sequential quadratic programming algorithm. The model is validated in test systems and then applied to a real case study. Results show the general applicability of the proposed model, with potential cost savings for the DisCo. Finally, the analysis of Lagrange multipliers gives valuable information, which can be used to improve the market design and to extend the use of the model to a more general market structure such as a power exchange.

A new method for distribution access via uniform pricing for the remuneration of distribution networks is presented. The proposed approach merges in a unified framework the investments, the optimal network operation requirements, the effect of the price elasticity of demand, and the application of hourly pricing for demand side management purposes. Hourly uniform marginal prices—understood as tariffs of use of the network—are obtained from maximum social welfare condition sending efficient signals to the utility and consumers, related to the optimal operation of the grid and use of the energy at peak and valley hours. This method is used in the context of a Performance Based Ratemaking regulation to get model companies from operational optimized real networks. Capital fees are integrated in the marginal tariff of use, by means of the New Replacement Value concept, broadly used in yardstick competition. The model is stated as a mixed-integer linear optimization problem suitable to be solved through well-known linear programming tools. The methodology has been successfully tested in a 42-bus test distribution network.

This paper addresses the allocation of electrical losses on distribution networks with embedded generation in a liberalized environment. The non-linear nature of the issue, the loss changes due to voltage variation and, specially, the contribution of embedded generation to losses variation are considered. The proposed method is based on tracing the real and imaginary parts of the currents. The losses of distribution network in the absence of embedded generation are allocated to the consumers (or their providers). The variations in the losses that result from the influence of embedded generation are allocated to the generators. These variations are a measure of avoided or incremented costs of losses that should be allocated to the embedded generators when designing tariffs. In the allocation process, made in a branch basis, both real and reactive power are considered.

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A nested Generalized Benders decomposition scheme is used to solve a mixed-integer stochastic programming model. The model evaluates central station and distributed power generation, storage, and demand management assets on a linearized electric power transmission network. It considers temporal and spatial variations in the marginal cost of power, which are captured in the Benders cuts in the solution scheme. These variations are caused not only by differences in generating unit operating expenses and capacity expansion costs, but also by physical transmission constraints that can alter minimum cost dispatch and siting of these units. The transmission constraints addressed include limits on MW power flows and both of Kirchhoff's laws via a linearized DC load flow representation. The model consists of three modules: a stochastic linear production costing model for operating central system generation, a nonlinear program for planning central system generation and transmission, and a mixed-integer program for evaluation of local area distributed resources. Generalized Benders decomposition is applied twice to coordinate these modules. The production costing model is a subproblem to the central system planning model, which is in turn a subproblem to the distributed resource model. The coordination scheme is described in detail, including the calculation of marginal costs. An application shows the effects of marginal cost variations on capacity expansion decisions.

This paper deals with optimal procurement of interruptible load services within secondary reserve ancillary service markets in deregulated power systems. The proposed model is based on an optimal power flow framework and can aid the independent system operator (ISO) in real-time selection of interruptible load offers. The structure of the market is also proposed for implementation. Various issues associated with procurement of interruptible load such as advance notification, locational aspect of load, power factor of the loads, are explicitly considered. It is shown that interruptible load market can help the ISO maintain operating reserves during peak load periods. Econometric analysis reveals that a close relationship exits between the reserve level and amount of interruptible load service invoked. It was also found that at certain buses, market power exists with the loads, and that could lead to unwanted inefficiencies in the market. Investing in generation capacity at such buses can mitigate this. The CIGRE 32-bus system appropriately modified to include various customer characteristics is used for the study.

A general formulation of the feeder reconfiguration problem for
loss reduction and load balancing is given, and a novel solution method
is presented. The solution uses a search over different radial
configurations created by considering switchings of the branch exchange
type. To guide the search, two different power flow approximation
methods with varying degrees of accuracy have been developed and tested.
The methods are used to calculate the new power flow in the system after
a branch exchange and they make use of the power flow equations
developed for radial distribution systems. Both accuracy analysis and
the test results show that estimation methods can be used in searches to
reconfigure a given system even if the system is not well compensated
and reconfiguring involves load transfer between different substations.
For load balancing, a load balance index is defined and it is shown that
the search and power flow estimation methods developed for power loss
reduction can also be used for load balancing since the two problems are
similar

This paper develops a quantification of the most important benefits brought about by the systematic operation of customer-owned back-up generators by distribution utilities or load-serving entities. The back-up reciprocating or internal combustion engine is the most widespread form of distributed generation, demonstrating high penetration levels in commercial and industrial areas. If the load-serving entity can oversee the operation of some of these units during strategic hours, important benefits can be realized, not only for the utility, but also for the owner of the unit and the society in general as shown in the results. The most important benefits of this coordinated operation are identified and quantified, using real cost data and pricing from a real North American electricity market.

This paper presents a multiperiod energy acquisition model for a distribution company (Disco) with distributed generation (DG) and interruptible load (IL) in a day-ahead electricity market. Assuming that cost information for individual generation companies (Gencos) and Discos is known, the Disco's energy acquisition strategy is modeled as a bilevel optimization problem with the upper subproblem representing individual Discos and the lower subproblem representing the independent system operator (ISO). The upper subproblem maximizes individual Discos' revenues. The lower subproblem simulates the ISO's market clearing problem that minimizes generation costs and compensation costs for interrupting load. The bilevel problem is solved by a nonlinear complementarity method. An 8-bus system is employed to illustrate the proposed model and algorithm. In particular, the roles of DGs and ILs to alleviate congestion are analyzed

This paper illustrates how a multiagent system (MAS)-based scheme can be developed for a control/optimization problem. The prototype problem considered is the dispatching of distributed generators on a distribution feeder to provide voltage support. The popular control net protocol (CNP) for MAS has been adopted to facilitate distributed control. This paper illustrates that characterization of the optimal solution is necessary in order to develop a protocol for the MAS to implement. This paper also shows that MAS facilitates a model-free control procedure, as it can monitor the local sensitivities. Test results, based on simulations on a prototype feeder, show that the proposed MAS-based control scheme is very effective in obtaining the solution for the prototype problem. The proposed method needs fast communication among the distributed generators (DGs) in order to assure fast response during emergency conditions. Communication requirements have also been identified in this paper

As distributed generation (DG) becomes more widely deployed, distribution networks become more active and take on many of the same characteristics as transmission. We propose the use of nodal pricing that is often used in the pricing of short-term operations in transmission. As an economically efficient mechanism, nodal pricing would properly reward DG for reducing line losses through increased revenues at nodal prices and signal prospective DG where it ought to connect with the distribution network. Applying nodal pricing to a model distribution network, we show significant price differences between buses reflecting high marginal losses. Moreover, we show the contribution of a DG resource located at the end of the network to significant reductions in losses and line loading. We also show the DG resource has significantly greater revenue under nodal pricing, reflecting its contribution to reduced line losses and loading.

This paper revisits the concept of a single slack bus in power flow solvers for distribution systems to accommodate the anticipated growth of distributed generators (DGs) in unbalanced distribution systems. It introduces a distributed slack bus model through scalar participation factors by applying the concept of generator domains. The participation factors are incorporated into the three-phase power flow equations, and a Newton-Raphson solver is discussed and implemented. Simulation results on systems with a different number of DGs and different levels of DG penetration are obtained and studied.

This paper proposes a new heuristic approach for distributed generation (DG) capacity investment planning from the perspective of a distribution company (disco). Optimal sizing and siting decisions for DG capacity is obtained through a cost-benefit analysis approach based on a new optimization model. The model aims to minimize the disco's investment and operating costs as well as payment toward loss compensation. Bus-wise cost-benefit analysis is carried out on an hourly basis for different forecasted peak demand and market price scenarios. This approach arrives at the optimal feasible DG capacity investment plan under competitive electricity market auction as well as fixed bilateral contract scenarios. The proposed heuristic method helps alleviate the use of binary variables in the optimization model thus easing the computational burden substantially.

This paper addresses the allocation of electrical losses in distribution networks with embedded generation, in a liberalized environment. The nonlinear nature of the issue, the loss changes due to voltage variation and, specially, the contribution of embedded generation to loss variation are considered. The proposed method is based on tracing the real and imaginary parts of the currents and has two steps. First, the losses in the distribution network, in the absence of embedded generation, are allocated to the consumers (or their providers). Second, the variations in the losses that result from the influence of embedded generation are allocated to the generators. These variations are a measure of the avoided or added costs related to losses. In the allocation process, made in a branch basis, both real and reactive powers are considered. The methodology presented in this paper can be used to evaluate embedded generation incentives or to design tariffs for the use of the distribution network.

Since 1994, Econnect has, in conjunction with Northern Electric
PLC, investigated the use of consumer load control as a new and
innovative method to actively regulate distribution system voltage when
affected by the-operation of embedded generators. There are a number of
issues that can limit the installed capacity of embedded generators;
these are often voltage related and the most common is steady-state
voltage rise. A number of techniques can be applied to limit
steady-state voltage rise, some of which are static in time (e.g.,
network re-inforcement) and some dynamic (e.g., power factor control).
This paper discusses the issue of excess steady-state voltage rise and
the methods of limitation that can be applied with specific reference to
wind generation. The new and innovative approach using consumer load
control is discussed and compared with the existing methods using a
simulation case study

Since 1994 Econnect has, in conjunction with Northern Electric PLC, investigated the use of consumer load control as a new and innovative method to actively regulate distribution system voltage when affected by the operation of embedded generators [1],[2]. There are a number of issues that can limit the installed capacity of embedded generators; these are often voltage related, and the most common is steady state voltage rise. A number of techniques can be applied to limit steady state voltage rise, some of which are static in time (e.g., network reinforcement) and some dynamic (e.g., power factor control). This paper discusses the issue of excess steady state voltage rise and the methods of limitation that can be applied with specific reference to wind generation. The new and innovative approach using consumer load control is discussed and compared with the existing methods using a simulation case study.