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The work presented in this paper describes a multi-issue negotiation protocol between active consumers and a management platform in order to establish load coordination in a highly congested network. The multi-issue negotiation protocol considers simultaneously the consumption interval, the price, and the size of the energy packages, which is the main contribution of this work. Regarding the implementation methodology, the proposed algorithms have been implemented using MATLAB classes that allow emulating the behaviour of a multi-agent system in which each vehicle is an agent that interacts with the platform, which is another agent. In the present work, and without loss of generality, the algorithm is applied to coordinate the charging of electric vehicles (EVs) in a distribution network in which building loads represent critical loads. The algorithm is tested in a realistic environment, and its stability and performance are evaluated. Furthermore, the description of the algorithm is provided in a generic form, and it could be applied to any other scenario. [ABSTRACT FROM AUTHOR]

Inter-regional power trading plays a crucial role in optimal resource utilization. However, intra-regional operation constraints are currently not well considered in practical implementation, which may lead to internal congestions and economic loss. In this paper, a non-iterative tie-line scheduling framework taking account of both intra- and inter-regional network constraints is proposed. Based on projection theory, admissible set of tie-line injections (ASTI) is defined as the allowable range of tie-line power injections that can be met by a regional system. Then, the ASTI is implemented in tie-line scheduling to ensure the feasibility of the tie-line schedule result for each region. Requirements for practical algorithms that calculate the ASTI in power system applications are analyzed, and a novel vertex-oriented projection algorithm is proposed to efficiently calculate the ASTI. Case studies based on several IEEE and Polish test systems verify the validity and efficiency of the proposed projection algorithm. Simulations based on a four-region system demonstrate the effectiveness of the proposed tie-line scheduling framework. [ABSTRACT FROM AUTHOR]

This paper develops a new electricity market clearing mechanism, called risk-based locational marginal pricing (RLMP). The RLMP is derived from the risk-based security-constrained economic dispatch model, where risk is modeled to capture the system's overall security level. Compared to the traditional locational marginal pricing (LMP) which is composed of three components: energy, congestion, and loss, the RLMP consists of an additional risk component. The risk component is a price signal to reflect the system's overall security level. This paper discusses the features of RLMP, and analyzes its benefits over traditional LMPs. The proposed concept and method are illustrated on a six-bus system and the WECC 240-bus system. [ABSTRACT FROM PUBLISHER]

Independent system operators (ISOs) are responsible for the secure and economic operation of the power systems. Accordingly, ISOs should solve an optimal power flow to minimize the operating cost of a system while meeting the system's security constraints. However, transmission congestion could drastically limit more economical generating units from being dispatched in the power system operation. In this paper, a decentralized transmission switching is introduced in the operation of multi-area power systems to manage the congestion both on regional transmission lines and tie lines while considering credible contingencies. Our proposed methodology relies on a decentralized optimization methodology. Thus, regional control centers in areas would be able to locally manage the congestion within their regional networks as well as tie lines connecting them to their neighboring areas. Several testing systems are studied in this paper to test the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Zonal pricing is a well-suited mechanism to incentivize grid-supporting behavior of profit-maximizing producers and consumers operating on a large-scale power system. In zonal electricity markets, local system operators create individual price zones, which provide appropriate price signals depending on local grid conditions such as an excess or shortage of electrical energy in certain regions. In this paper, a real-time zonal pricing controller for AC power networks is presented that ensures frequency and voltage stability as well as Pareto efficiency of the resulting closed-loop equilibria. Based on a dynamic network model that takes line losses and power exchange with adjacent price zones into account, distributed continuous-time control laws are derived which require only neighbor-to-neighbor communication. Application to a real-time congestion management strategy illustrates how spatially and temporally differentiated prices enable grid-supportive operation of the participants without interventions by a superordinate control authority. Effectiveness of different zonal pricing concepts compared to an isolated grid operation is demonstrated through simulations on the IEEE-57 bus system. [ABSTRACT FROM AUTHOR]

Buildings are candidates for providing flexible demand due to their high consumption and inherent thermal inertia. In the future, flexible demand side reserves may also help to relax the expected higher reserve requirements of the grid due to the presence of renewables. However, this flexible demand might be vulnerable to price signals, as the simultaneous increase in consumption by multiple buildings due to low (high) energy (reserves) price periods might cause congestion in distribution grids. In order to integrate congestion-free energy and reserve provision from buildings, this paper presents two benchmark pricing methodologies: (1 ) distribution locational marginal prices (DLMP), and (2) iterative DLMP (iDLMP). Both methods deploy convex optimization to obtain an optimal solution of the original problem. Using dual decomposition, a settlement scheme, which efficiently distributes the congestion cost among involved participants, is also presented. Case studies are performed on a benchmark distribution system along with the National Energy Market Singapore's price framework. The results prove that both methods optimally remove congestion from distribution grids and have potential to be integrated into the theoretical framework of liberalized markets. Furthermore, as a comparison, it is shown that the DLMP-based prices outperforms existing pricing structures of the distribution grid. Hence, using this scheme, the distribution system operator can evaluate existing tariffs and introduce incentives for price responsive demands. However, to support these methods, the high requirement for information sharing in the DLMP method and/or communication technology infrastructure for calculating iDLMPs must exist in the future grid. [ABSTRACT FROM AUTHOR]

Demand response (DR) can be counted as an effective congestion management method in power systems. However, appointing the optimal buses for the demand response programs (DRPs) implementation is one of the main challenges for the power system operators. In this paper, a new procedure has been developed by which the optimal locations and times of DRPs implementation are determined. Optimal buses are identified based on the power transfer distribution factors (PTDFs), available transfer capability, and dynamic dc optimal power flow problem. Applying the developed method results in reducing lines’ congestion, increasing customers, and independent system operator's benefits, improving load curve's characteristics, preventing line outages and black outs, and consequently increasing the network reliability. The proposed method has been applied on the IEEE 39-bus New England test system. Results indicate the effectiveness and practical benefits of the proposed method. [ABSTRACT FROM AUTHOR]

This paper addresses the congestion management problem avoiding offline transmission capacity limits related to stability. These limits on line power flows are replaced by optimal power flow-related constraints that ensure an appropriate level of security, mainly targeting voltage instabilities, which are the most common source of stability problems. Results from an illustrative case study based on the IEEE 24-bus Reliability Test System are analyzed. Conclusions are duly drawn. [ABSTRACT FROM AUTHOR]

The European Union is targeting a 20% share of energy from renewable resources by 2020, and this increase is in turn expected to lead to operational challenges that will require various congestion management actions by system operators. In this paper, we deal with topology control of the transmission network as a congestion management resource and evaluate the impacts of topology control on the European electricity network. To do this, we co-optimize unit commitment and transmission switching over 24 h, and we use a decomposition scheme to tackle the resulting large-scale problem. Our analysis is conducted on different scenarios of load and renewable power generation. We find that topology control results in significant cost savings within Europe which tend to be inversely related to net load. The robustness of our results is supported by an extensive sensitivity analysis. [ABSTRACT FROM AUTHOR]

This paper presents the distribution locational marginal pricing (DLMP) method through quadratic programming (QP) designed to alleviate the congestion that might occur in a distribution network with high penetration of flexible demands. In the DLMP method, the distribution system operator (DSO) calculates dynamic tariffs and publishes them to the aggregators, who make the optimal energy plans for the flexible demands. The DLMP through QP instead of linear programing as studied in previous literatures solves the multiple solution issue of the aggregator optimization which may cause the decentralized congestion management by DLMP to fail. It is proven in this paper, using convex optimization theory, the aggregator's optimization problem through QP is strictly convex and has a unique solution. The Karush-Kuhn-Tucker (KKT) conditions and the unique solution of the aggregator optimization ensure that the centralized DSO optimization and the decentralized aggregator optimization converge. Case studies using a distribution network with high penetration of electric vehicles (EVs) and heat pumps (HPs) validate the equivalence of the two optimization setups, and the efficacy of the proposed DLMP through QP for congestion management. [ABSTRACT FROM PUBLISHER]

In this paper, a day-ahead market-clearing model for smart distribution systems is proposed. Various types of distributed energy resources (DERs), such as distributed energy storage, distributed generators, microgrids, and load aggregators, can bid into the day-ahead distribution-level electricity market. Considering system Volt/VAR control, network reconfiguration, and interactions with the wholesale market, an optimization model is built to clear the day-ahead market, through which the distribution locational marginal pricing (DLMPs) for both active power and reactive power are determined. Through derivations of the Lagrangian function and sensitivity factors, DLMPs are decomposed to five components (i.e., marginal costs for active power, reactive power, congestion, voltage support, and loss), which provide price signals to motivate DERs to contribute to congestion management and voltage support. Finally, case studies demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Increasing Photovoltaic (PV) penetration and low-carbon demand can potentially lead to two different flow peaks, generation, and load, within distribution networks. This will not only constrain PV penetration but also pose serious threats to network reliability. This paper uses energy storage (ES) to reduce system congestion cost caused by the two peaks by sending cost-reflective economic signals to affect ES operation in responding to network conditions. First, a new charging and discharging (C/D) strategy based on binary search method is designed for ES, which responds to system congestion cost over time. Then, a novel pricing method, based on locational marginal pricing (LMP), is designed for ES. The pricing model is derived by evaluating ES impact on the network power flows and congestions from the loss and congestion components in LMP. The impact is then converted into an hourly economic signal to reflect ES operation. The proposed ES C/D strategy and pricing methods are validated on a real local grid supply point area. Results show that the proposed LMP-based pricing is efficient to capture the feature of ES and provide signals for affecting its operation. This work can further increase network flexibility and the capability of networks to accommodate increasing PV penetration. [ABSTRACT FROM PUBLISHER]

High congestion costs and new congestion patterns, formed by intermittent renewable generation, demand for more efficient utilization of the transmission system. Transmission switching (TS) and variable-impedance series FACTS devices are two technologies that provide such efficiency gains through controlling the power flows. As the system operators begin to utilize these tools, it is essential to understand the interdependence between them at various stages, such as planning and operation. This paper develops an optimal power flow framework that incorporates both FACTS and TS to study the link between the two power flow control technologies. The simulation results on an RTS 96-bus system suggest that: 1) substantial economic savings can be achieved through utilization of both TS and FACTS, beyond the independent capabilities of each technology; 2) performing TS actions affect the optimal location and set point of FACTS devices; and 3) operation of FACTS devices affect the location and frequency of TS actions. It is, thus, essential to acknowledge the interdependence between the two technologies both at the planning and operation stage. Failure to do so will lead to economic inefficiencies that can be avoided through co-optimization of generation dispatch, TS, and FACTS. [ABSTRACT FROM PUBLISHER]

The recent rise of electricity generation based on renewable energy sources increases the demand for transmission capacity. Capacity expansion via the upgrade of transmission line capacity, e.g., by conversion to a high-voltage direct current (HVDC) line, is an attractive option. In this paper, it is shown that if the upgrade to HVDC is applied systematically to selected transmission lines across the grid, a hybrid architecture is obtained that enables an efficient and globally optimal solution of the optimal power flow (OPF) problem. More precisely, for conventional meshed AC transmission grids the OPF problem is nonconvex and in general NP-hard, rendering it hard to solve. We prove that after the upgrade to the proposed hybrid architecture, the same mesh topology facilitates an exact convex relaxation of the OPF problem, enabling its globally optimal solution with efficient polynomial time algorithms. This OPF method is then employed in simulations, which demonstrate that the hybrid architecture can increase the effective transmission capacity and substantially reduce the generation costs, even compared to the AC grid with optimal transmission switching. [ABSTRACT FROM PUBLISHER]

The dynamic tariff (DT) method is designed for the distribution system operator (DSO) to alleviate congestions that might occur in a distribution network with high penetration of distributed energy resources (DERs). Uncertainty management is required for the decentralized DT method because the DT is determined based on optimal day-ahead energy planning with forecasted parameters such as day-ahead energy prices and energy needs which might be different from the parameters used by aggregators. The uncertainty management is to quantify and mitigate the risk of the congestion when employing the DT method, which is achieved by firstly formulating the problem as a chance constrained two-level optimization and then solving the problem through an iterative procedure. Two case studies were conducted to demonstrate the efficacy of the uncertainty management of DT method. [ABSTRACT FROM PUBLISHER]

In market-based planning and operation, it is very useful to have the information of generation dispatch, congestion, and price as load increases. Therefore, it is beneficial to system planners if the congestion or locational marginal pricing (LMP) versus load is readily available. This can certainly be obtained by repetitively running an optimization model at different load levels. However, this approach is too brute-force to be practical. In this paper, an efficient algorithm is proposed to identify the new binding constraint and the new marginal unit set when the system load increases from the present load level. It addresses the challenge of step changes in generation dispatch when a generation or transmission limit becomes binding. The algorithm also gives the new sensitivity of the new marginal units. Therefore, the generation dispatch, congested lines, and LMP at a new critical load level (CLL) can be easily calculated. Test results are presented in matrix formulation to clearly demonstrate and verify the proposed algorithm. Since the proposed approach is based on linearized model, it should be particularly suitable for short-term planning or operation, although application to long-term planning is also possible. [ABSTRACT FROM AUTHOR]

In a zonal market, the transmission system operator (TSO) has to compute the transfer limits among areas in advance (weeks or months) with respect to the day-ahead market session. The computation of such limits is usually made starting from some reference scenarios: this choice is arbitrary and has a strong influence on the results of the market. In this paper, a new probabilistic approach is developed to reduce such arbitrariness. A Monte Carlo method is applied to sample many different reference scenarios (in terms of generation patterns) to be adopted for the total transfer capacity (TTC) computation. Eventually, the probability density function of the TTC values is built. The proposed procedure allows the TSO to evaluate, for each possible choice of the TTC limit among areas, the maximum probability of congestion in a market framework, thus selecting the limit corresponding to the acceptable risk level,, The new methodology is applied to the Italian system. [ABSTRACT FROM AUTHOR]

When transmission bottlenecks affect dispatches and prices of neighboring Regional Transmission Organization (RTO) markets, intermarket congestion management and coordination are needed to mitigate congestion and to compute economically efficient prices in the day-ahead markets. This paper discusses and proposes two decentralized processes with which each RTO administers its energy market and also acts as a transmission coordinator to achieve feasible and efficient use of congested transmission by all markets in the region (interconnection). With this arrangement, a new decomposition technique is introduced without breaking a network model at the RTO's geographical boundaries. The paper then presents two decentralized congestion management formulations to model markets interactions in the interconnection. The coordination between market operators and transmission operators is based on the use of congestion price signals or allocated megawatt capacities, as well as their sensitivity information. The proposed solution ensures not only feasible schedules but also efficient and consistent congestion path prices for the final schedules in the entire interconnection. Numerical examples are given to illustrate the application of the proposed models. [ABSTRACT FROM AUTHOR]

Independent System Operators have difficulty in fulfilling all contractual power transactions in a competitive energy market due to transmission network congestion. As a result, applications of generator rescheduling become one of the antidotes in alleviating this difficulty in the consequence of ever-increasing numerous power transactions. The goal of this research is to lower the cost of active and reactive power of the generators by reducing the deviation of rescheduled active and reactive power from scheduled values. The inclusion of reactive power rescheduling and voltage stability in this paper is innovative, as compare to other existing methodologies solely examine active power rescheduling. This paper made the following contributions: formulated a multi-objective function for congestion control in an electric transmission network. Furthermore, formulated the generator sensitivity factors to identify overloaded lines and which generators will be involved in congestion management. Developed a particle swarm optimization (PSO) algorithm to solve the multi-objective function of the transmission congestion management system. In addition, the developed PSO method for CM approach was validated on three IEEE standard test system networks (14, 30, and 118). The simulation results prove that reduces active and reactive power, lowering the cost of generator rescheduling, and demonstrating the usefulness of developed PSO method for transmission network congestion. Furthermore, voltage stability and voltage profile improvements demonstrate the performance effectiveness of the PSO algorithm used in this work.

This paper presents a game-theoretic approach for solving network congestion in the Smart Grid. A smart grid environment with high penetration of Electric Vehicles (EVs) for a bidirectional power trade provides an opportunity to use the EVs for congestion management. Optimal power injected or drawn by the EV charging stations can be obtained to mitigate the congestion problem by solving a constrained optimization problem to minimize the change in the power injected at every bus while satisfying several constraints. EV charging stations will be requested to inject or draw optimal power as obtained from the optimization problem. This paper proposes a game-theoretic approach (Greed Game) to develop a smart pricing model and design a mechanism to remove congestion in the network. A unique Nash equilibrium is achieved in the game, which optimally fulfills the line power congestion management problem and yields maximum benefit for the EVs. The algorithm was applied to 16-machine 68-bus test systems, and it has been found that the proposed mechanism maximizes the gain of the EVs and solves the congestion problem in the Grid.

Large-scale and ubiquitous penetration of wind power generation to power systems necessitates more conservative provision of system reliability by ensuring adequately committed reserve and observance of transmission constraints. In addition, wind power curtailment due to the technical limitations of system operations, such as transmission congestion, should be efficiently mitigated. To this aim, this paper presents a congestion risk-aware unit commitment formulation in a two-settlement market environment. The uncertainty impact of multicorrelated wind power and contingencies on the risk of transmission congestion for each line, called the Line Transfer Margins (LTM), is incorporated using basic statistical data on the nodal wind power forecast and probability of credible line-outages across the system. The LTMs, formulated free of any distributional assumptions, collectively provide a measure for transmission reserves, which effectively mitigate the likelihood of transmission congestion, reserve undeliverability, and wind power curtailment in the real-time economic dispatch. The effectiveness of the proposed approach is verified through comparative case studies on IEEE RTS-96 for various wind power and LTM scenarios. [ABSTRACT FROM AUTHOR]

This paper gives the background and role of the market flow in the interregional congestion management process for market-based operating entity and describes the market flow methodology. It proposes that the market flow method calculates the same impact result on a flowgate as the unit dispatch system (UDS) flow does and proves it mathematically. The equivalence of market flow and UDS flow shows that the UDS flow change on a constraint due to the UDS binding will have the same effect on the change in the market flow under the electricity market operations. In addition, it proves that, in an interconnected system, the sum of the market flows of all of the market entities, the generation-to-load impacts of nonmarket entities, and the total tagged transaction impact among the entities is equal to the dc flow for any constraint. The assessment of market flow methodology demonstrates the effectiveness of using the market flow in the congestion management process and coordinated management of transmission constraints. The proposed methodology can help identify the cause of loop flow problem in power systems. The numerical results on a 23-bus test system and a 7917-bus system are also illustrated. [ABSTRACT FROM PUBLISHER]

This paper focuses on the construction of an optimized financial transmission rights (FTR) or congestion revenue rights portfolio for an FTR market participant given his assessment of the frequency and economic impacts of binding constraints in the transmission network. We overcome the data handling and heavy computing demands of locational marginal price (LMP)-difference-based methods for FTR selection by recasting the problem into one that focuses on the underlying product of “binding constraints”, which are physically observable phenomena, based on the mathematical insights into the structural characteristics of the model used for the clearing of the hourly day-ahead markets. Differentials in the LMPs are due to system congestion and so are merely manifestations of binding constraints in the transmission network. In addition, we exploit extensively the salient topological characteristics of large-scale interconnections. The market participant specifies the subset of “focus” constraints and the position he is willing to take on them. Our approach builds on the mathematical insights and topological characteristics with the effective deployment of the orthogonal matching pursuit algorithm to construct the optimized FTR portfolio characterized by the minimum number of node pairs for the specification of the FTR elements. We apply the proposed approach to a test system based on the PJM ISO network and markets to illustrate its capabilities for solving the FTR market participant's problem in realistic large-scale systems. [ABSTRACT FROM PUBLISHER]

Recently, we proposed a capacity expansion approach for transmission grids that combines the upgrade of transmission capacity with a transition in system structure to improve grid operation. The key to this concept is a particular hybrid AC/DC transmission grid architecture, which is obtained by uprating selected AC lines via a conversion to HVDC. We have shown that this system structure improves optimal power flow (OPF) solvability and that it can reduce the total generation costs. In this paper, we study the benefits of this hybrid architecture in the context of a deregulated electricity market. We propose an efficient and accurate nodal pricing method based on locational marginal prices (LMPs) that utilizes a second-order cone relaxation of the OPF problem. Applicability of this method requires exactness of the relaxation, which is difficult to obtain for conventional meshed AC transmission grids. We prove that the hybrid architecture ensures applicability if the LMPs do not coincide with certain pathological price profiles, which are shown to be unlikely under normal operating conditions. Using this nodal pricing method, we demonstrate that upgrading to the hybrid architecture can not only increase the effective transmission capacity, but also reduce the separation of nodal markets and improve the utilization of generation. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of price responsive demand shifting bidding on congestion and locational marginal prices in pool-based day-ahead electricity markets. The market dispatch problem of the pool-based day-ahead electricity market is formulated as to maximize the social welfare of market participants subject to operational constraints given by real and reactive power balance equations, and security constraints in the form of apparent power flow limits over the congested lines. The social welfare objective function of the day-ahead market dispatch problem maximizes the benefit of distribution companies and other bulk consumers based on their price responsive demand shifting bids and minimizes the real and reactive power generation cost of generation companies. The price responsive demand shifting bidding mechanism, which has been recently introduced in the literature, is able to shift the price responsive demand from the periods of high price to the periods of low price in day-ahead electricity markets. The comparisons of the price responsive demand shifting bids with conventional price responsive and price taking bids are presented by solving hourly market dispatch problems on five-bus, IEEE 30-bus, realistic UP 75-bus Indian, and IEEE 118-bus systems for 24-h scheduling period. It has been demonstrated that the proposed approach leads to reduction in congestion and locational marginal prices as compared to price responsive and price taking bids and meets the energy consumption targets of distribution companies/bulk consumers. [ABSTRACT FROM PUBLISHER]

The possibility for market participants to simultaneously place their bids in different markets across an interconnection is investigated in this paper. Transaction schedulers settle multilateral transactions among participants, while a central entity coordinates the overall operation through interactions with the transaction schedulers. Two issues are dealt with in this context. First, the market participants are allowed to place their bids simultaneously in more than one transactions scheduler's market, and, second, the available transmission capacity is fairly shared among the transaction schedulers. Economically interesting transactions are favored, while confidentiality of market data and independence of transaction schedulers' clearing mechanisms are preserved. The corresponding iterative algorithm is illustrated in detail on a 15-bus as well as the IEEE-RTS system. [ABSTRACT FROM AUTHOR]

Both QoS support and congestion management techniques become essential to achieve good network performance in current high-speed interconnection networks. The most effective techniques traditionally considered for both issues, however, require too many resources for being implemented. In this paper, we propose a new cost-effective switch architecture able to face the challenges of congestion management and, at the same time, to provide QoS. The efficiency of our proposal is based on using the resources (queues) used by RECN (an efficient Head-of-Line blocking elimination technique) also for QoS support, without increasing queue requirements. The provided results show that the new switch architecture is able to guarantee QoS levels without any degradation due to congestion situations. [ABSTRACT FROM AUTHOR]

This paper analyzes the implementability of locational marginal price (LMP) and financial transmission right (FTR) mechanisms in the presence of high voltage dc (HVDC) lines in the regulated (i.e., under the control of system operator) network. The established framework of LMP-FTR mechanism assumes that the system under regulation is fully ac. Unregulated (or private) HVDC lines are taken into account within this framework through proxy-transaction bids from the line owners. However, as the flow over a regulated HVDC line has to be controlled by the system operator himself, the existing LMP-FTR framework becomes insufficient if the system contains or is expanded with regulated HVDC lines. The augmentation that should be made to the existing framework to include regulated HVDC lines is explained in this paper. It is shown that the flow over a regulated HVDC line can be modeled as a non-chargeable bilateral transaction without causing any threat of negative congestion surplus. For this power dispatch model, revenue adequacy of FTRs can be still ensured by means of the conventional simultaneous feasibility test (SFT) model. By utilizing the capacities of dc lines, a more general SFT model is further developed. The modified auction problem is discussed and tested for the non-negativity of net auction collection. The process of revenue adequate issuance of auction revenue rights is explained. Finally, the relative merit of a regulated HVDC line over a private HVDC line is discussed through a case study. [ABSTRACT FROM AUTHOR]

This paper proposes a decentralized model for dc load flow based congestion management for the forward markets via Optimal Resource Allocation (ORA). The available thermal capacities of possible congested transmission lines are considered as commonly shared resources for all bilateral and multilateral transactions in the market. In our model, each transaction maximizes its profit under the limits of transmission line capacities allocated by the ISO. The ISO searches the optimal allocation of line capacities to each transaction. Finally, the same market efficiency (social welfare) as centralized optimization can be reached. The ORA-based decentralized approach is more suitable for power market environment, since it always keeps the intermediate solution feasible during the iterations and does not require each transaction to submit their private and sensitive information. The mathematical model, computation procedure and relevant proof are presented. The computer test results from the IEEE 30 bus system illustrate the effectiveness of proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents an effective method of congestion management in power systems. Congestions or overloads in transmission network are alleviated by generation rescheduling and/or load shedding of participating generators and loads. The two conflicting objectives 1) alleviation of overload and 2) minimization of cost of operation are optimized to provide pareto-optimal solutions. A multiobjective particle swarm optimization (MOPSO) method is used to solve this complex nonlinear optimization problem. A realistic frequency and voltage dependent load flow method which considers the voltage and frequency dependence of loads and generator regulation characteristics is used to solve this problem. The proposed algorithm is tested on IEEE 30-bus system, IEEE 118-bus system, and Northern Region Electricity Board, India (NREB) 390-bus system with smooth as well as nonsmooth cost functions due to valve point loading effect. [ABSTRACT FROM AUTHOR]

The concept of electrothermal coordination (ETC) in power system operation introduced in Part I proposes to exploit thermal inertia to coordinate line temperature dynamics with existing power system controls, thus increasing power transfer capability and enhancing system security and economic performance. In this Part II, the characteristics of ETC and its benefits in operational functions such as augmenting power transfer capability, emergency control, congestion management, and system loadability are numerically analyzed through a number of case studies. This paper also examines some practical issues concerning the deployment of ETC. [ABSTRACT FROM AUTHOR]

The security criteria of a power system require that branch power flows and bus voltages are within their limits, not only in normal operating conditions but also when any credible contingency occurs. In the Spanish electricity market, voltage constraints are solved by connecting a set of offline generators located in the areas where they occur. Thus, for a market participant it is necessary to predict approximately when its generating units are connected in order to prepare the annual budget and/or decide the time and location of new plants. This paper proposes a methodology based on decision trees to estimate the daily load pattern of units that have not been cleared in the daily energy market and can be connected to alleviate the network constraints in the Spanish power system. The method explains the behavior of the daily load patterns of a nonconnected unit (obtained through clustering techniques) with a set of explanatory variables. The explanatory variables consist of the demand-generation imbalance in the electrical area of the generating unit and the maintenance scheduling of the transmission lines that feed the area. The method proposed is illustrated with a case study. [ABSTRACT FROM AUTHOR]

The solution of power system constraints is an important issue that has to be addressed to achieve a fair operation of a competitive electricity market. In the Spanish electricity market, the System Operator (SO) is in charge of determining the technical feasibility of the generation dispatch provided by the Market Operator (MO). An optimal solution method of power system constraints in the Spanish market must take into account the connection of off-line units to solve both branch overloads and voltage constraints, the adjustment of voltage control resources, the solution of the postulated n-I and n-2 contingencies with a preventive criteria and the coupling of the solution in the 24 hourly scenarios due to the start-up cost of nonconnected units. This paper details the management of power system constraints in the Spanish market, and contains a comprehensive review of solution methods of power system constraints. In addition, the paper proposes a novel approach to solve Spanish power system constraints that overcomes the limitations of existing methods, by decoupling the solution of branch overloads and voltage constraints. [ABSTRACT FROM AUTHOR]

The capability to deal effectively with the uncertainty associated with locational marginal prices (LMPs) in congestion management schemes requires the development of appropriate financial tools. Congestion revenue rights (CRR) are hedging tools that provide the holder reimbursement of the congestion charges in the day-ahead market and thereby provide transmission service customers with price certainty. In this paper, we construct a framework for the design and analysis of the CRR by marrying finance theory notions with salient characteristics of electric power systems and electricity markets. The framework consists of three interconnected layers with one layer each to represent the model of the transmission network, the commodity markets and the CRR financial markets. The interaction between the layers is represented as information flows. The framework has sufficient cope to allow the analysis of a broad range of problems associated with ensuring price certainty for transmission services. The structural modularity of the framework provides the flexibility to analyze issues and design structures for the provision of transmission services in the competitive environment. We introduce a new notion of CRR payoff parity and a practical pricing scheme, which are used as the basis for the design of more liquid CRR markets. The application of the framework is further illustrated by the analysis of the conditions that guarantee the revenue adequacy for the CRR issuer. [ABSTRACT FROM AUTHOR]

Due to the capacity limitation and the renewable energy's uncontrollability, multiple energy hubs (EHs) are connected by energy routers (ERs) to form a network structure and further build the future energy internet. With concave objective functions and/or nonlinear operation constraints, the centralized optimal dispatch of interconnected EHs is difficult to solve. In order to reduce the complexity of centralized optimal dispatch on large-scale systems and protect the information privacy of different EHs, a hierarchical optimization strategy is put forward for the ER-based energy internet. Specifically, the lower level is the optimal dispatch within EHs, where the dispatch model of EH is further optimized by considering variable dispatch factors, and the information privacy of different EHs is preserved. The upper level is the energy routing control between EHs, where the end-to-end power transmission according to contract path is realized through the controllability of ERs and energy routing protocols. Finally, the effectiveness of the hierarchical optimization strategy and model are verified by simulations. [ABSTRACT FROM AUTHOR]

This paper presents an integrated distribution locational marginal pricing (DLMP) method designed to alleviate congestion induced by electric vehicle (EV) loads in future power systems. In the proposed approach, the distribution system operator (DSO) determines distribution locational marginal prices (DLMPs) by solving the social welfare optimization of the electric distribution system which considers EV aggregators as price takers in the local DSO market and demand price elasticity. Nonlinear optimization has been used to solve the social welfare optimization problem in order to obtain the DLMPs. The efficacy of the proposed approach was demonstrated by using the bus 4 distribution system of the Roy Billinton Test System (RBTS) and Danish driving data. The case study results show that the integrated DLMP methodology can successfully alleviate the congestion caused by EV loads. It is also shown that the socially optimal charging schedule can be implemented through a decentralized mechanism where loads respond autonomously to the posted DLMPs by maximizing their individual net surplus. [ABSTRACT FROM PUBLISHER]

As parallel computing systems increase in size, the interconnection network is becoming a critical subsystem. The current trend in network design is to use as few components as possible to interconnect the end nodes, thereby reducing cost and power consumption. However, this increases the probability of congestion appearing in the network. As congestion may severely degrade network performance, the use of a congestion management mechanism is becoming mandatory in modern interconnects. One of the most cost-effective proposals to deal with the problems derived from congestion situations is the Regional Explicit Congestion Notification (RECN) strategy, based on using special queues to totally isolate the packet flows which contribute to congestion, thereby preventing the Head-of-Line (HoL) blocking effect that these flows may cause to others. Unfortunately, RECN requires the use of source-based routing, thus not being suitable for interconnects with distributed routing, like InfiniBand. Although some RECN-like mechanisms have been proposed for distributed-routing networks, they are not scalable due to the huge amount of control memory that they require in medium-size or large networks. In this paper, we propose Distributed-Routing-Based Congestion Management (DRBCM), a new scalable technique which, following the RECN principles, totally prevents congestion from producing HoL-blocking in multistage interconnection networks (MINs) using tag-based distributed routing. Simulation results indicate that, regardless of network size, DRBCM presents small resource requirements to keep network performance at maximum level even in scenarios of heavy congestion, where it utterly outperforms (with a gain up to 70 percent) current solutions for distributed-routing networks, like the InfiniBand congestion-control mechanism based on injection throttling. Thus, DRBCM is an efficient, cost-effective, and scalable solution for congestion management. [ABSTRACT FROM AUTHOR]

There has been an increasing demand for connectivity of the clusters of microgrids to increase their flexibility and security. This paper presents a framework for implementation, simulation, and evaluation of a novel power routing algorithm for clusters of microgrids. The presumed cluster is composed of multiple direct current (dc) microgrids connected together through multi-terminal dc system in a meshed network. In this structure, the energy is redirected from the microgrid with excessive power generation capacity to the microgrid which has power shortage to supply its internal loads. The key contribution of this paper is that each microgrid in the cluster is unaware of the current state and other flows of the cluster. In this approach, the optimal power flow problem is solved for the system while managing congestion and mitigating power losses. The proposed methodology works for both radial and non-radial networks regardless of the network topology, scale, and number of microgrids involved in the cluster. Therefore, it is also well suited for large-scale optimal power routing problems that will emerge in the future clusters of microgrids. The effectiveness of the proposed algorithm is verified by MATLAB simulation. We also present a comprehensive cloud-based platform for further implementation of the proposed algorithm on the OPAL-RT real-time digital simulation system. The communication paths between the microgrids and the cloud environment can be emulated by OMNeT++.

In the locational marginal price (LMP)-based congestion management scheme, transmission customers face uncertainty in the congestion charges they incur. In order to bring certainty to customers, congestion revenue rights (CRR) such as the fixed transmission rights (FTR) used in the PJM interconnection and flow- gate rights (FGR) are introduced. These CRR are financial tools that provide the holder reimbursement of the congestion charges incurred in the day-ahead market. The implementation of CRR requires ,appropriate modeling of the transmission network in which the distribution factors are extensively used. These factors-the injection shift factors (ISFs) and the power transfer distribution factors (PTDFs)-are linear approximations of the sensitivities of the active power line flows with respect to various variables. The factors are computed for a specified network topology and parameter values. In practice, the PTDFs used for the CRR issuance may be different from those used in the day-ahead market due to changes in the forecasted network conditions. The PTDF errors may impact the FTR issuance quantities, the revenue adequacy of the FTR issuer and the hedging ability of the FGR. In this paper, we explore analytical characteristics of these distribution factors and investigate their role in CRR applications. We study the nature of the PTDF errors and examine their impacts in these applications, both analytically and experimentally. Our results indicate that the impacts of the PTDF errors in CRR applications stay in an acceptable range under a broad spectrum of conditions including contingencies used to establish n - 1 security. [ABSTRACT FROM AUTHOR]

The rapid growth of inter-regional trading among electricity markets requires the development of new market-oriented mechanisms for the inter-regional congestion management of such trading. In this paper, we deal with the operation of power systems consisting of several interconnected electricity markets. We propose an alternative approach to inter-regional trade that avoids the flaws of forward markets with explicit auctioning of interconnections capacities. We propose the Integration of a forward market with a balancing (spot) market for inter-regional exchanges based on nodal pricing. The Interaction of transmission system operators (TSOs) belonging to adjacent markets is efficiently taken into account through a decentralized optimal power flow (OPF), which is solved by Interior point methods. [ABSTRACT FROM AUTHOR]

Addressing the trends of digitalization, decentralization, democratization, and decarbonization, local peer-to-peer (P2P) markets have the potential to significantly accelerate decarbonization at the communal level. However, due to an increase in the number of energy consumers, such as electric vehicles or heat pumps, grid congestion can occur since actual low-voltage grids are not designed to transmit large loads. This paper introduces a novel concept for a platform to combine the advantages of P2P trading with the need for secure, automated low-voltage grid control, ensuring effective congestion management. Therefore, a dual local energy market has been developed, comprising a P2P energy market and a flexibility market with the latter ensures preventively managing congestion. Furthermore, mechanisms exist to provide curative, real-time congestion management. Additionally, the platform empowers prosumers, i.e. end users that produce and consume electrical energy, with intelligent market strategies to maximize their financial outcomes by participating in both markets. To provide a secure trading mechanism, the novel concept of Self-Sovereign-Identity is integrated into the platform. The platform is based on a multi-agent-system developed using the Java Development Environment (JADE) in conjunction with the Energy Option Model (EOM) for the effective modelling of energy systems. Tested in a smart grid laboratory at the University of Wuppertal, the platform provides financial gains for prosumers and effectively manages current- and voltage-related grid congestions.

This paper proposes a distribution locational marginal pricing (DLMP) based bi-level Stackelberg game framework between the internet service company (ISC) and distribution system operator (DSO) in the data center park. To minimize electricity costs, the ISC at the upper level dispatches the interactive workloads (IWs) across different data center buildings spatially and schedules the battery energy storage system temporally in response to DLMP. Photovoltaic generation and static var generation provide extra active and reactive power. At the lower level, DSO calculates the DLMP by minimizing the total electricity cost under the two-part tariff policy and ensures that the distribution network is uncongested and bus voltage is within the limit. The equilibrium solution is obtained by converting the bi-level optimization into a single-level mixed-integer second-order cone programming optimization using the strong duality theorem and the binary expansion method. Case studies verify that the proposed method benefits both the DSO and ISC while preserving the privacy of the ISC. By taking into account the uncertainties in IWs and photovoltaic generation, the flexibility of distribution networks is enhanced, which further facilitates the accommodation of more demand-side resources.

The distribution grid faces several challenges related to the decarbonisation of the economy, which require incorporating flexibility services alongside traditional grid reinforcement solutions to enable an efficient grid development. Flexibility services, as well as the needs that require them, are very diverse. Therefore, general estimations about the value of flexibility applicable to any given scenario are unfeasible or imprecise. This paper reviews the literature on the quantification of the value of flexibility and proposes a broad-spectrum methodology aligned with the actual challenges of the energy transition for the planning of distribution network as it includes a comprehensive analysis of the real costs and the type of needs. Based on it, four representative and realistic case studies compare the BAU (business as usual) solutions with flexibility services analysing the technical and economic perspectives. Results show that flexibility value depends on the case studies considered and that, under certain circumstances, BAU solutions can be more competitive than alternatives with flexibility services. Network reinforcements in the distribution network have a long lifespan and provide a reliable service to thousands of customers. However, flexibility services can be particularly useful for accelerating decarbonisation with flexible connections or short-term solutions to manage the distribution network operation.

This paper handles the problem of controlling speed limits on freeways in a connected traffic environment to reduce traffic congestion and improve both the operational and environmental performance of the road network. In order to achieve this objective, we present a Variable Speed Limit (VSL) system that utilizes fuzzy logic, which adjusts the speed limits that connected vehicles must comply with by leveraging traffic data such as vehicle flow, occupancy, and speed obtained from loop detectors installed along the road. To evaluate the effectiveness of the proposed Fuzzy-based VSL system and its potential benefits compared to the conventional rule-based VSL system in terms of traffic congestion and environmental impact, we conducted a simulation analysis using the microscopic traffic simulator, VISSIM. Specifically, three simulation scenarios are taken into account: $i)$ no VSL, where the VSL system is not enabled; $ii)$ Rule-based VSL system, where a typical a decision tree-based system is considered; $iii)$ Fuzzy-based VSL system, where the herein proposed approach is appraised. The results demonstrate that the proposed approach enhances road efficiency by decreasing speed variation, increasing average speed and vehicle volume, and reducing fuel consumption.

The new technologies emerging in the energy sector pose new requirements for both the regulation and the operation of the electricity grid. Revised tariff structures and the introduction of local markets are two approaches that could tackle the issues resulting from the increasing number of active end-users. However, a smooth transition from the traditional schemes is critical, thus creating the need for solutions that can be implemented in the current circumstances. This paper proposes a local market concept and a corresponding dynamic tariff system, which can not only be operated parallel to the current retail market, but which also considers possible interactions between the two markets by taking into account the estimated state of the network. The participants of the market can trade energy peer-to-peer via a platform that allocates proper network charges to all transactions. The calculated tariffs consider the physical effect of the transactions on the grid in terms of nodal voltage deviations, branch current flows, and overall system losses. The proposed method is tested and compared to the currently existing local market approach on the IEEE European LV test feeder through market simulations. The results imply that knowledge on network state prior to trades can increase the correctness of network charge allocation. With the proper tuning of DNUT (Dynamic Network Usage Tariff) components, the end-users can even realize larger surplus compared to other local market models, while the security of network operation is also ensured.

The growing integration of renewable energies into electricity grids leads to an increase of grid congestions. One countermeasure is the curtailment of renewable energies, which has the disadvantage of wasting energy. Forecasting congestion provides valuable information for grid operators to prepare and instruct countermeasures to reduce these energy losses. This paper presents a novel approach for congestion prediction in distribution grids (i.e. up to 110 kV) considering the n-1 security criterion. For this, our method considers node injections and power flow and combines three artificial neural network models. The analysis of study results shows that the implemented neural networks within the presented approach perform better than naive forecasts models. In the case of vertical power flow, the artificial neural networks also show better results than comparable parametric models: average values of the mean absolute errors relative to the parametric models range from 0.89 to 0.21. A high level of accuracy can be achieved for the neural network that predicts the loading of grid components with a F1 score of 0.92. Further, also with a F1 score of 0.92, this model shows higher accuracy for the distribution grid components than for those of the transmission grid, which achieve a F1 score of 0.84. The presented approaches show good potential to support grid operators in congestion management.

We introduce emerging utility-scale energy storage (e.g., batteries) as part of the set of control measures in a corrective form of the security-constrained unit commitment (SCUC) problem. This enhanced SCUC (ESCUC) leverages utility-scale energy storage for multiple applications. In the base case, the storage units are optimally charged and discharged to realize economic operation. Immediately following a contingency, the injections of storage units are adjusted almost instantly to alleviate short-term emergency overloads, thereby avoiding potential cascading outages and giving slow ramping generating units time to adjust their output. The ESCUC is a large two-stage mixed-integer programming problem. A Benders decomposition has been developed to solve this problem. In order to achieve computational tractability, we present several acceleration techniques to improve the convergence of the proposed algorithm. Case studies on the RTS-79 and RTS-96 systems demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]