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Due to the acceleration of energy transition from fossil fuels to Distributed Energy Resources (DERs), the structure of the power industry (mainly generation and distribution) is changing. The emergence of the smart grid concept and Peer-to-Peer (P2P) electricity markets make local producers and consumers face new challenges and risks. Managing the risks that the participants in P2P markets are encountered is necessary to guarantee a sustainable penetration of such markets. This article provides a comprehensive risk analysis by implementing a proposed risk management framework to address the identification, classification, assessment, and mitigation of all risks that prosumers will face when participating in a P2P electricity market. The main risk factors have been investigated in an elaborated way through risk assessment and preliminary suggestions for risk mitigation. Ten risks have been identified and categorized into three main groups. The proper potential responses are addressed for each assessed risk to mitigate the impact of risk associated with participation in the P2P electricity market. Studying the risks from a prosumer's point of view, considering management of those risks in the context of micro-grid scheduling/optimization, and designing the mechanism of the P2P electricity trading market will enhance the acceptance of participating in these markets. Moreover, further research opportunities and directions regarding risk management concepts in P2P electricity markets are discussed. • Comprehensive risk management framework of Peer-to-Peer electricity market is provided. • 10 Major risk factors in 3 categories are identified, assessed, and analyzed. • Risk mitigation strategies are suggested in order to motivate prosumers to participate in Peer-to-Peer markets. • Business model risk is recognized as the most important risk factor. [ABSTRACT FROM AUTHOR]

This paper presents a two‐stage adaptive robust optimization framework for day‐ahead energy and intra‐day flexibility self‐scheduling of a technical virtual power plant (TVPP). The TVPP exploits diverse distributed energy resources' (DERs) flexibility capabilities in order to offer flexibility services to wholesale flexibility market as well as preserving the distribution network's operational constraints in the presence of DER uncertainties. The TVPP aims at maximizing its profit in energy and flexibility markets considering the worst‐case uncertainty realization. In the proposed framework, the first stage models the TVPP's participation strategy in day‐ahead energy market and determines the DERs' optimal energy dispatch. The second stage addresses the TVPP's strategy in intra‐day flexibility market to determine the DERs' optimal flexibility capability provision by adjusting their energy dispatch for the worst‐case realization of uncertainties. The uncertainty characteristics associated with photovoltaic units, electric vehicles, heating, ventilation and air conditioning systems, and other responsive loads as well as the transmission network's flexibility capability requests are considered using an adaptive robust approach. Adopting the duality theory, the model is formulated as a mixed‐integer linear programming problem and is solved using a column‐and‐constraint generation algorithm. This model is implemented on a standard test system and the model effectiveness is demonstrated. [ABSTRACT FROM AUTHOR]

In market-based planning studies, the financial aspects of transmission grids have been posed as a major challenge since these are important keys to provide a non-discriminative and competitive environment for energy market participants. In this respect, this paper proposes a probabilistic multi-objective transmission expansion planning algorithm considering private investors' preferences, technical concerns, and random nature of power system uncertainties. The combination of Non-Dominated Sorting Genetic Algorithm II, as a widely-used robust multi-objective optimization technique, and point estimation method is employed to handle non-commensurable probabilistic objective functions. The IEEE Reliability Test System is taken into consideration as the test system to verify and address the different aspects of the proposed method. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

With the increasing rate of population in big cities around the world, the tendency to build new buildings in the suburb of main cities or to build large apartments in the main cities has been highlighted. In this regard, building residential complexes has seen a dramatic increase in these areas as it makes it possible to build a large number of residential units within a reasonable space. Although these complexes have brought numerous benefits, they are some challenges regarding their construction processes. One main concern associated with these complexes is how to optimally install energy components such as transformers, combined heat and power (CHP) units, boilers etc., in the shared area of apartments in the residential complex. To address this issue, this paper models the energy system of a residential complex as an energy hub and proposes a novel framework to obtain the optimal planning of such an energy hub. In order to address the conflicting desires of the residential complex's builders and the future residents of the residential units, a multi‐objective (MO) optimization problem has been considered in the proposed method that simultaneously optimizes the investment costs, operation costs, and the reliability of energy supply. In this regard, a Multi‐objective Particle Swarm Optimization (MOPSO) algorithm combined with classical linear programming (LP) optimization method has been proposed to solve the MO optimization problem. In order to demonstrate the effectiveness of the proposed method, a case study including a residential complex with 300 residential units is considered, and the proposed method is implemented in this case study. The numerical results show that the proposed framework can appropriately optimize investment costs, operation costs, and the reliability index simultaneously, and the obtained Pareto frontier gives the investors the freedom to opt for any point from this surface. [ABSTRACT FROM AUTHOR]

Unpredictable occurrence of natural disasters in modern power systems has significantly increased the attentions to the outage management of multi‐microgrid (MMG) systems. In this regard, a multi‐objective two‐stage outage management scheme is presented in this paper to protect undamaged microgrids (MGs) against load curtailment and minimize the curtailed loads simultaneously. Considering the importance of power system economic, this scheme is designed in a privacy‐preserved transactive energy (TE) market and enables all MGs to participate in outage management voluntarily. In detail, a DC optimization is autonomously operated in the first stage in each MG to supply its demand loads. Then, the second stage is designed to exchange the surplus power of the last stage with the damaged MGs. These voluntary power transactions are pre‐specified in contracts, adjusted between each MG and distributed system operator (DSO). Moreover, in this stage, the ADMM algorithm based on DC‐OPF has prepared a privacy protection platform and proved the results' accuracy. The presented test grid is the IEEE 34‐node test system and the numeric results demonstrate the effectiveness of the proposed multi‐objective scheme. [ABSTRACT FROM AUTHOR]

Growing demand for flexible ramp products (FRPs) to maintain the power balance in active distribution networks (ADNs) has expanded the use of costly fast-start generators in the upstream real-time market. In order to limit the power system's reliance on these units, it is necessary to establish new sources for providing ADNs with FRPs. In this context, the present paper examines the abilities of distributed energy resources (DERs) and active loads for supplying the ramp capacity demands of ADNs. Accordingly, a local real-time market was established in the ADN, empowering several DERs and active loads to provide the network's FRPs demands. The proposed market was formed in the context of a multi-period security constraint economic dispatch (SCED) problem. Besides, the uncertainties associated with renewable generation units and load consumption in the ADN were addressed by robust optimization. The proposed multi-period SCED problem was formulated linearly and in two stages. The first stage specifies the optimal dispatch results for the binding interval based on the network's current conditions, whereas an adaptive robust SCED problem is handled in the second stage considering future periods. The local market developed within a two-stage robust multi-period SCED problem was then solved using a column-and-constraint generation method selected for its computational efficiency. Finally, the proposed market was studied on the modified 33-bus distribution network. The results indicated that DERs have sufficient capacity to support the ADN ramp capacity requirement. • Network's dependency on the upstream system for supplying ramp capacity is reduced. • The proposed market brings financial gains for the active distribution network. • Local energy resources and loads have enough abilities to supply ramp capacities. • Maximum capacity and given price affect on a share of units in operating reserve. [ABSTRACT FROM AUTHOR]

This paper presents a new framework for multistage expansion planning in active power distribution networks, in which the distribution system operator (DSO) considers active network management by clearing the local energy market at the distribution level. The proposed model is formulated as a bi-level optimization problem, where the upper level minimizes the net present value of the total costs imposed to DSO associated with the investment and maintenance of the network assets as well as the network operation, while the lower level on clearing the local energy market captures the participation of distributed energy resource (DER) owners and demand aggregators to maximize the social welfare. The expansion plans consider the investments in DER owners’ assets as well as variety of network assets in which the profitability of DER owners’ investment is guaranteed. The Karush-Kuhn-Tucker optimality conditions and the strong duality theory are employed through which the model is converted to a mixed integer linear programming optimization problem. The implementation of the suggested model on the 24-bus and 86-bus distribution test systems validates its performance and efficacy in making cost-effective planning decisions. [ABSTRACT FROM AUTHOR]

• To solve the issues related to ignoring network constraints in the peer matching process, we apply NUF based on sensitivity analysis to evaluate the impact of each agent's equivalent load on network constraints. The proposed NUF can fully and fairly diagnose the portion of each agent on power loss, congestion and voltage violation. • This work extends the existing primal-dual subgradient algorithm by adding Peer-to-Grid (P2G) energy trading and NUF. • The proposed TEM framework would innovatively ensure the individual TE agent privacy while facilitating energy trading. • As part of the proposed TEM framework, the DSO operation (e.g., VVC and network reconfiguration) is considered, which may assist in providing the optimal network topology to facilitate peer matching and minimize interference with the scheduling of the TE agents. A growing number of distributed energy resources (DERs) connected to the distribution network presents operational challenges. Therefore, it is crucial to develop an effective framework to address these challenges. In this paper, a novel transactive energy market (TEM) framework is proposed in two levels based on the concept of a network usage fee (NUF) to address technical issues. We perform the peer matching process using a primal-dual algorithm at a lower level. We consider distribution system operation (DSO) operations (e.g., Volt/VAR control and network reconfiguration) and NUF to maintain network constraints and to provide an optimal network topology to facilitate energy trading. The proposed framework has been tested on an IEEE 33-bus distribution network. A key issue relating to the coordination of DERs in distribution networks is addressed in the results. In the proposed NUF, the sensitivity analysis is used to fully diagnose the portion of each TE agent in power loss, congestion, and voltage violations. A fair penalty or incentive signal can be generated to ensure that the network operates within the limits. Furthermore, the proposed framework ensures that the privacy of agents is protected. DSO operation facilitates the trading of energy and minimizes interference with their schedules. [ABSTRACT FROM AUTHOR]

• Developing a novel modeling of zonal congestion management method based on the demand response. • Introducing a new congestion index to form the flexible congestion control zone. • Determining the optimal incentives in the demand response programs based on the congestion alleviation. • Investigating the effects of dynamic lines rating on the congestion. Congestion is one of the main obstacles to fair competition in today's electricity markets. In this regard, a set of rules are legislated to create a fair competitive environment and ensure adequate control over consumers to maintain an acceptable level of reliability with maximum economic efficiency. In this paper, using power tracing techniques and sensitivity analysis, a new dynamic zonal congestion management (ZCM) model based on the demand response program (DRP) is developed. The congestion control zones are determined by an introduced congestion index (CI) including some candidates with both high effectiveness in congestion alleviation (the sensitivity analysis) and high responsibility in the congestion creation (the power tracing technique). Then, through the intelligent implementation of DRP in the zone, a conceptual framework with a comprehensive solution method for congestion alleviation is proposed. Throughout the proposed partitioning based DRP, the optimal amount of load decrease and incentives are determined considering economic issues. In power system operation, contingency analysis such as generator and line outages and uncertainty of lines capacities are inevitable, so they will be taken into account in our proposed methodology. The capacity fluctuates a lot due to the environmental conditions and it will be shown through a comparative study that dynamic line rating (DLR) has a significant impact on results. Using the proposed method, the system is simplified and power system operators can put their focus on the candidate zone than the whole network and alleviate the congestion effectively in the least possible time. The proposed methodology is applied on IEEE 118-bus test system and the results show the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

• Introducing a multi-layer multi-objective planning framework of a residential complex energy system considering reliability indexes. • Considering both the energy hub investors' and the resident's desires in the optimal planning of the energy hub by addressing these conflicting desires by the proposed multi-objective framework and the Pareto frontiers. • Addressing the possible regulatory incentives and tariffs in the planning framework. • Employing PSO algorithm to solve the multi-objective problem and to introduce the Pareto frontiers. Optimal planning of residential complex energy systems requires thorough mathematical modelling to address the interconnections between all the energy installations from the largest ones, shared by all the residents, to the smallest ones in each distinct unit. Besides, conflicting desires of investors and residents in various aspects such as reliability index make this problem more challenging. In response, this paper presents a thorough framework to obtain the optimum design and operation of a residential complex energy system from scratch. To address the appropriate interconnection between various components of such an energy system, a multi-layer energy hub structure is proposed. Besides, the proposed optimization problem consists of a multi-objective function to consider several criteria such as reliability, operational costs, and investment costs to address both investors' and residents' desires appropriately. A Multi-objective Particle Swarm Optimization (MOPSO) algorithm is implemented to obtain the optimum solutions for the developed optimization problem, and a set of Pareto optimal solutions are introduced. By doing so, investors can accurately analyse the interrelation among different objectives such as reliability, investment costs, and operational costs of the residential complex and choose an optimum solution based on their priorities. To demonstrate the effectiveness of the proposed method, it is applied to a case study consisting of a residential energy system with 300 units. The numerical results indicate that such a framework not only reduces total operation and investments costs by 24% but also increases reliability, continuity of energy supply, significantly in comparison to other common methods such as single-layer EH and single-objective EH planning frameworks. [ABSTRACT FROM AUTHOR]

Increasing the intermittent outputs of renewable energy sources (RESs) has forced planners to define a new concept named flexibility. In this regard, some short‐ and long‐term solutions, such as transmission expansion planning (TEP) and energy storage systems (ESSs) have been suggested to improve the flexibility amount. A proper optimization procedure is required to choose an optimal solution to improve flexibility. Therefore, a mixed‐integer linear programming (MILP) direct‐optimization TEP versus ESSs co‐planning model is presented in this paper to enhance power system flexibility. In doing so, a novel RES‐BESS‐based grid‐scale system flexibility metric is proposed to investigate the improvement of flexibility amount via ESSs modules in the numerical structure. In this paper, a novel repetitive fast offline method has been proposed to quickly reach the desired amount of flexibility by defining an engineering price/benefit trade‐off to finally find the best investment plan. Also, multiple uncertainties associated with wind farms and demanded loads and a practical module‐type battery energy storage system (BESS) structure for each node are defined. The proposed model is applied to the modified IEEE 73‐bus test system including wind farms, where the numerical results prove the model efficiency as BESS impacts on flexibility, investment plans and power system economics. [ABSTRACT FROM AUTHOR]