Ivana Kockar

Dynamic Optimal Power Flow for Active Distribution Networks

Active Network Management is a philosophy for the operation of distribution networks with high penetrations of renewable distributed generation. Technologies such as energy storage and flexible demand are now beginning to be included in Active Network Management (ANM) schemes. Optimizing the operation of these schemes requires consideration of inter-temporal linkages as well as network power flow effects. Network effects are included in optimal power flow (OPF) solutions but this only optimizes for a single point in time. Dynamic optimal power flow (DOPF) is an extension of OPF to cover multiple time periods. This paper reviews the generic formulation of DOPF before developing a framework for modeling energy technologies with inter-temporal characteristics in an ANM context. The framework includes the optimization of nonfirm connected generation, principles of access for nonfirm generators, energy storage, and flexible demand. Two objectives based on maximizing export and revenue are developed and a case study is used to illustrate the technique. Results show that DOPF is able to successfully schedule these energy technologies. DOPF schedules energy storage and flexible demand to reduce generator curtailment significantly in the case study. Finally, the role of DOPF in analyzing ANM schemes is discussed with reference to extending the optimization framework to include other technologies and objectives.

Phase shifter placement in large-scale systems via mixed integer linear programming

This paper makes use of advances in mixed integer linear programming (MILP) to conduct a preliminary design study on the combinatorial optimal placement of thyristor controlled phase shifter transformers (TCPSTs) in large-scale power systems. The procedure finds the number, network location, and settings of phase shifters that maximize system loadability under the DC load flow model, subject to limits on the installation investment or total number of TCPSTs. It also accounts for active flow and generation limits, and phase shifter constraints. Simulation results are presented for the IEEE 24-, 118-, and 300-bus systems, as well as a 904-bus network. The principal characteristics of our approach are compared with those of other published flexible AC system transmission (FACTS) allocation methods.

Distribution power flow management utilising an online Optimal Power Flow technique

This paper describes the current connection regime for distributed generation (DG) in the U.K. and presents a novel application of the optimal power flow (OPF) technique for automatic power flow management (PFM) to manage thermal constraints in distribution networks. OPF formulations have been used, in an offline mode, as a power system planning tool for several years. The novel implementation of OPF for “corrective” PFM in an online operational mode, for MV distribution networks, is presented and tested in this paper. The authors demonstrate, through simulations conducted on a commercially available substation computer, that such an application of OPF can represent first on, last off generator connection agreements that reflect the current principles of access in the U.K. Two case study networks, a 33 kV and an 11 kV, provide the basis for assessment of the OPF-based PFM algorithm in terms of computation time to arrive at a solution in the event of a network thermal excursion and the level of DG curtailment necessary to meet network thermal limits. Assessments are made and fully discussed of the suitability for an OPF-based approach for distribution network management within an online network control scheme including discussion of the important consideration of control robustness.

Combined pool/bilateral dispatch. I. Performance of trading strategies

This three-paper series deals with the dispatch of power networks under mixed pool/bilateral trading. The major questions examined are (i) to what degree does the relative level of pool versus bilateral trading influence performance in terms of individual power levels, costs, prices, revenues and expenditures; (ii) what is the comparative performance of mixed trading with firm and nonfirm bilateral contracts under various curtailment strategies; and (iii) is the revenue derived from the pool and bilateral trading consistent with the corresponding unbundled costs? The above issues are sequentially addressed in each of the three parts. The eventual goal of these results is to help generator and load-serving entities choose appropriate relative levels of pool versus bilateral trades while considering risk, economic performance, as well as physical constraints. This paper proposes a one-step optimal power flow model that dispatches the pool in combination with the privately negotiated bilateral contracts while minimizing cost and accounting for both losses and congestion. In part I, notions of pool/bilateral demand and generation as well as a number of technical and economic performance measures for each competing entity are defined. This dissection of total and individual financial measures according to pool or bilateral trading allows the market participant to evaluate the profitability of each component of its chosen pool/bilateral mix. A number of simulation results illustrate the effect of varying the relative levels of pool/bilateral trading on the values of individual performance measures.

Reducing Distributed Generator Curtailment Through Active Power Flow Management

This paper presents an evaluation of the main characteristics of two power flow management (PFM) methodologies against a traditional inter-trip approach typically used by distribution network operators. The two PFM algorithms were developed, by the authors, for real-time operation with an aim to implement them in distribution networks with growing penetrations of renewable DG. The first PFM approach is modelled as a constraint satisfaction problem (CSP), while the second is based on an optimal power flow (OPF) approach. These PFM algorithms are deployed on real substation hardware to simulate the monitoring and control of MV distribution network power flows through DG real power regulation. Multiple scenarios are presented to the closed-loop PFM test environment to demonstrate the algorithms ability of detecting and alleviating thermal overloads and recognizing when the constraint has passed. The main objective of this paper is the quantification of the resultant curtailment levels, for the two approaches, which are compared to that of a traditional inter-trip scheme for the same circuit overload duration. The results demonstrate that taking an active approach to managing power flows can significantly increase the output of DG units in a thermally constrained network.

The optimal operation of energy storage in a wind power curtailment scheme

Generator curtailment allows Distribution Network Operators to increase the maximum capacity of distributed renewable generation connections to their networks, but curtailment means lost revenue for generators. Energy Storage Systems (ESS) can mitigate curtailment by time-shifting generation away from congested periods and can combine this with other tasks. This paper develops a linear-programming optimization to maximize the revenue generated by an ESS connected to a wind farm in a curtailment scheme. The storage is used for curtailment reduction and price-arbitrage in an external market. A case study is developed and the optimization applied for storage devices with a range of efficiencies and penetrations. The effect of storage efficiency on revenue is shown to be stronger in price arbitrage than in generation-curtailment. An economic analysis is carried out for a Sodium Sulphur battery store and it is clear that, at current costs, more valuable revenue streams are required to achieve economic viability.

Unit commitment for combined pool/bilateral markets with emissions trading

The paper proposes a unit commitment problem formulation which models combined pool/bilateral operation as well as effects of emissions constraints and emissions trading mechanisms. In here proposed model, power outputs of generators are, on one hand, bounded by their bilateral contract commitments, and on the other side, by the amount of CO2 emissions that they are allowed to produce over time. These emissions constraints introduce additional complexity as it is becoming more important for generating units to decide on when and how much to produce, as well as to investigate how to mange their allocated CO2 permissions in the most economic way. Since the proposed formulation of the market clearing includes costs of buying and selling of CO2 allowances, it permits analysis on how emission caps and emission market prices can influence generation decisions and the resulting generation scheduling. The method is illustrated on a 5-unit system, and given examples investigate how different levels of bilateral trades, as well as amounts of CO2 allowances, may affect the market outcome.

Agent-based modeling of the demand-side flexibility

The paper presents an agent-based approach to model the flexibility of the demand-side. It uses Q-learning algorithm to model a behavior of a demand-side agent, so to investigate the elasticity of the demand to the change in price. Often, market simulation models assume that the demand elasticity is known, however due the lack of data this elasticity is not easy to determine. The objective of this paper is to evaluate the flexibility of the total system demand, and the shift in the consumption with the price, i.e. increase in the demand when the price is low, and a decrease in the demand when the price is high. The here presented model of a demand-side agent is incorporated into the market simulator with double-sided auctions, and is tested on the Slovenian market. However, this approach can be used to estimate flexibility in any system for which the forecasted demand data and generation offers are know.

Northern Isles New Energy Solutions: Active network management stability limits

The Northern Isles New Energy Solutions (NINES) project is addressing the current and future energy needs of the Shetland Isles by demonstrating the integration of low carbon energy sources using smart grid technology. In so doing, NINES will facilitate a major step towards a low carbon future for Shetland whilst leading and informing the wider international low carbon energy transition. The principal objective of the NINES project is to enable more renewable connections in a geographical area that is deemed to have the richest renewable energy resources in Europe. As such, the electrically islanded Shetland power network will see significant changes in operation as district heating schemes, domestic space and water heating systems, energy storage systems and new wind connections are developed, deployed and integrated under an active network management system. This paper discusses the role of inter-dependent system models in providing essential inputs to active network management (ANM) design and configuration. Early results from model development and testing are presented with specific focus on the stability limits for the connection of additional renewable generation when operating in conjunction with frequency responsive demand.

Influence of emissions trading scheme on market clearing and prices

This paper and the presentation will compare Emissions Trading Scheme in the European Union and the recently established Regional Greenhouse Gas Initiative in the US, looking at their similarities and differences, as well as results that are available so far. In addition, the paper investigates effects that emissions constraints may have on market clearing prices in electricity markets. The analysis is based on a two step procedure in which the emissions generation scheduling problem is solved first, and then its solution is used in the dynamic optimal power flow problem that also accounts for emissions constraints though augmented cost function that includes possible purchases or sales of emissions allowances on the market. This formulation allows for investigating how decisions of generators on how to use their CO2 emission allocations over a period of time may affect market outcome and prices.