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The flexibility in transmission networks is not fully utilized in existing energy management systems (EMSs). Corrective transmission switching (CTS) is proposed in this two-part paper to enable EMS to take advantage of the flexibility in transmission systems in a practical way. This paper proposes the following two EMS procedures: first, Procedure-A connects real-time security-constrained economic dispatch (RT SCED) with real-time contingency analysis (RTCA), which is consistent with industrial practice; and second, Procedure-B, an enhanced version of Procedure-A, includes CTS in EMS with the proposed concept of branch pseudo limit used in RT SCED. Part-I of this paper presents the methodology, whereas Part-II includes detailed results analysis. It is demonstrated that Procedure-A can effectively eliminate the base case overloads and the potential post-contingency overloads identified by RTCA, and Procedure-B can achieve significant congestion cost reduction with consideration of CTS in RT SCED. Numerical simulations also illustrate that integrating CTS into RT SCED would improve social welfare. [ABSTRACT FROM AUTHOR]

This paper presents a novel procedure for energy management system (EMS) that can utilize the flexibility in transmission networks in a practical way. With the proposed enhanced EMS procedure, the reliability benefits that are provided by corrective transmission switching (CTS) in real-time contingency analysis can be translated into significant cost savings in real-time security-constrained economic dispatch. Simulation results show that the congestion cost with consideration of CTS is largely reduced as CTS can relieve potential post-contingency network violations. The effects of integrating CTS in existing EMS procedure on markets are also analyzed. In conclusion, this two-part paper shows that CTS can achieve substantial reliability benefits, as well as significant cost savings. [ABSTRACT FROM AUTHOR]

This paper modifies the traditional security-constrained unit commitment (UC) model to include a reserve policy that aims to preemptively anticipate post-contingency congestion patterns and account for uncertainty, simultaneously. The policy uses post-contingency transmission constraints to predict the influence of recourse actions under critical generator contingencies and to cover a range of uncertain scenarios by defining reserve response factors, which are determined offline using a data-mining algorithm. The main motive is to address both the locational and the deliverability issues that are usually associated with reserve. The performance of the proposed data-driven reserve response set policy is compared against two sets of deterministic reserve policies and an extensive form stochastic UC model. All simulations are conducted on a modified 2383-bus Polish test system. Test results illustrate that the proposed model consistently outperforms the benchmark policies by improving the market efficiency and enhancing the reliability of the market solution while maintaining scalability and transparency. The proposed model can be employed by contemporary solvers with minimal disruption to existing market procedures. [ABSTRACT FROM AUTHOR]

The uncertainty and variability associated with stochastic resources coupled with the constantly changing system operating conditions introduce new challenges to power systems. Smart, well-designed reserve policies are needed to assist the operators in maintaining system reliability. This paper presents a reserve response set model, which improves upon existing deterministic models. The proposed model aims to address the allocation and deliverability issues associated with reserves by using reserve response set policies and by modeling the predicted post-contingency effects of nodal reserve deployment on critical transmission elements. The performance of the proposed reserve model is compared against contemporary deterministic programs and an extensive-form stochastic program. The results show that the proposed reserve model outperforms the contemporary models and improves the deliverability of reserves post-contingency at reduced costs. All numerical results are based on the IEEE 118-bus and the 2383-bus Polish test systems. [ABSTRACT FROM AUTHOR]

Reserve requirements serve as a proxy for N-1 reliability in the security-constrained unit commitment (SCUC) problem. However, there is no guarantee that the reserve is deliverable for all scenarios (post-contingency states). One cheap way to improve reserve deliverability is to harness the flexibility of the transmission network. Flexible AC transmission system (FACTS) devices are able to significantly improve the transfer capability. However, FACTS utilization is limited today due to the complexities these devices introduce to the DC optimal power flow problem (DCOPF). With a linear objective, the traditional DCOPF is a linear program (LP); when variable impedance based FACTS devices are taken into consideration, the problem becomes a nonlinear program (NLP). A reformulation of the NLP to a mixed integer linear program, for day-ahead corrective operation of FACTS devices, is presented in this paper. Engineering insight is then introduced to further reduce the complexity to an LP. Although optimality is not guaranteed, the simulation studies on the IEEE 118-bus system show that the method finds the globally optimal solution in 98.8% of the cases. Even when the method did not find the optimal solution, it was able to converge to a near-optimal solution, which substantially improved the reliability, very quickly. [ABSTRACT FROM PUBLISHER]

Reliable and economic deployment of a generation fleet to satisfy demand is a complex problem. Independent system operators (ISOs) and regional transmission operators (RTOs) solve complex unit commitment and economic dispatch models to determine appropriate resources to deploy at various time stages. Due to the complexity of power systems, several approximations are made within optimization models, including approximations of the transmission network with a linearized formulation known as the direct current optimal power flow (DCOPF) instead of the more realistic formulation known as the alternating current optimal power flow (ACOPF). Furthermore, approximations occur in these models by relaxing specific constraints in the model, i.e., the constraint is allowed to be violated based on a predetermined penalty price. By doing so, the ISO/RTO receives several benefits, including the ability to manage prices, clear the market, as well as the potential to obtain gains in social welfare (market surplus). This paper describes the constraint relaxation practices of ISOs in their unit commitment models and analyzes the corresponding market implications based on these practices. [ABSTRACT FROM PUBLISHER]

In order to ensure a reliable and continuous supply of electric energy, operators must manage hundreds to thousands of generators with complex operating requirements, maintain synchronism, and manage thousands of transmission assets. Further complications arise due to stringent reliability standards, resource uncertainty, and limited economical energy storage. With advances in algorithmic performance and hardware, such optimization problems still require an engineered and simplified market structure. Market operators must therefore adjust the proposed market solution in order to obtain a valid solution. An overview of existing industry practices related to such market adjustments, referred to as out-of-market corrections, is presented. This paper also analyzes the market implications and required out-of-market corrections for the day-ahead scheduling process. Multiple out-of-market correction algorithms are created and used to analyze the potential impacts on market solutions and settlements. Finally, such solutions are compared against an extensive form stochastic unit commitment solution. [ABSTRACT FROM PUBLISHER]

Static reserve policies are used within security-constrained unit commitment (SCUC) and security constrained economic dispatch (SCED) to ensure reliability. A common policy is that 10-min reserve must exceed the largest contingency. However, this condition does not guarantee reliability because voltage and thermal limits can hinder reserve deliverability. Many operators use zonal reserve markets to ensure reserves are dispersed across the grid. Such zonal models attempt to anticipate transmission bottlenecks, which is a difficult task when the future system state is uncertain. This paper examines the market implications of dynamic reserve policies used to mitigate uncertainty from renewable resources and contingencies. We study the market implications of policies recently proposed in the literature, such as hourly zones within day-ahead SCUC and an algorithm that formally disqualifies reserves that are expected to be undeliverable. A locational reserve pricing scheme is also proposed in connection with scenario-based reserve disqualification. Analysis on the RTS-96 test case shows that dynamic zones and reserve disqualification, along with the proposed compensation scheme, help direct reserve payments toward resources that more effectively respond to contingencies. [ABSTRACT FROM PUBLISHER]

As more non-dispatchable renewable resources are integrated into the grid, it will become increasingly difficult to predict the transfer capabilities and the network congestion. At the same time, renewable resources require operators to acquire more operating reserves. With today's deterministic reserve requirements unable to ensure optimal reserve locations, improvements to reserve policies are needed to ensure reserve deliverability while maintaining a reliable system at least cost. This paper proposes a daily reserve zone determination procedure, which is able to reflect system operating conditions by utilizing probabilistic power flows. A statistical clustering algorithm is used to cluster buses together to produce the zones; the algorithm uses a centrality measurement, which is based on weighted power transfer distribution factors. The proposed method is validated by testing it on a modified IEEE 118-bus system for multiple days; the proposed method is compared against existing reserve zone partitioning procedures. While the proposed reserve zone determination method is a heuristic, it is shown to be effective and it is a computationally tractable method. The proposed method can be used on its own and can be used along with stochastic programming techniques that implicitly determine reserves. [ABSTRACT FROM PUBLISHER]

Reserve requirements promote reliability by ensuring resources are available to rebalance the power system following random disturbances. However, reliability is not guaranteed when dispatch is limited by transmission constraints. In this work, we propose a modified form of reserve requirement that identifies response sets for distinct contingency scenarios. The approach disqualifies reserve from counting towards a particular scenario if transmission constraints are likely to render that reserve undeliverable. A decomposition algorithm for security-constrained unit commitment dynamically updates the response sets to address changing conditions. Testing on the RTS 96 test case demonstrates the approach applied in tandem with existing reserve policies to avoid situations where reserve is not deliverable due to transmission constraints. Operational implications of the proposed method are discussed. [ABSTRACT FROM PUBLISHER]

The security-constrained unit commitment problem schedules power generators to minimize cost subject to operating constraints. Reserve requirements ensure that reserve is available to re-balance the system following a contingency but do not guarantee reliability in congested systems where reserve activation is limited by transmission constraints. Operators improve reserve deliverability by limiting power flows and controlling the location of reserves. Zones are used to address inter-zonal congestion but operators lack efficient ways to handle intra-zonal congestion. We propose dynamically relating minimum reserve levels to transmission stress in a computationally efficient approach that complements existing reserve models from the literature. Analysis on the IEEE RTS 96 test case demonstrates the approach can mitigate intra-zonal congestion more economically than traditional reserve policies. [ABSTRACT FROM PUBLISHER]

There is currently a national push to create a smarter grid. Currently, the full control of transmission assets is not built in network optimization models. With more sophisticated modeling of transmission assets, it is possible to better utilize the current infrastructure to improve the social welfare. Co-optimizing the generation with the network topology has been shown to reduce the total dispatch cost. In this paper, we propose the concept of just-in-time transmission. This concept is predicated on the fact that transmission that is a detriment to network efficiency can be kept offline when not needed and, with the proper smart grid/advanced technology, can be switched back into service once there is a disturbance. We determine which lines to have offline based on the optimal transmission switching model previously proposed. A secondary topic of this paper focuses on flowgate bidding. Approved by the Federal Energy Regulatory Commission and implemented within the SPP and NYISO networks, flowgate bidding is defined as allowing a line's flow to exceed its rated capacity for a short period of time for a set penalty, i.e., price. We demonstrate the effectiveness of these models by testing them on large-scale ISO network models. [ABSTRACT FROM AUTHOR]