Advanced Studies on Locational Marginal Pricing

The effectiveness and economic aspect of Locational Marginal Price (LMP) formulation to deal with the power trading in both Day-Ahead (DA) and Real-Time (RT) operation are the focus of not only the system operator but also numerous market participants. In addition, with the ever increasing penetration of renewable energy being integrated into the grid, uncertainty plays a larger role in the process of market operation. The study is carried out in four parts. In the first part, the mathematical programming models, which produce the generation dispatch solution for the Ex Post LMP, are reviewed. The existing approach fails to meet the premise that Ex Post LMP should be equal to Ex Ante LMP when all the generation and load combinations in RT operation remain the same as in DA market. Thus, a similar yet effective approach which is based on a scaling factor applied to the Ex Ante dispatch model is proposed. In the second part, the step change characteristic of LMP and the Critical Load Level (CLL) effect are investigated together with the stochastic wind power to evaluate the impacts on the market price volatility. A lookup table based Monte Carlo simulation has been adopted to capture the probabilistic nature of wind power as well as assessing the probabilistic distribution of the price signals. In the third part, a probability-driven, multilayer framework is proposed for ISOs to schedule intermittent wind power and other renewables. The fundamental idea is to view the intermittent renewable energy as a product with a lower quality than dispatchable power plants, from the operator’s viewpoint. The new concept used to handle the scheduling problem with uncertainty greatly relieves the intensive computational burden of the stochastic Unit Commitment (UC) and Economic Dispatch (ED). In the last part, due to the relatively high but similar R/X ratio along the radial distribution feeder, a modified DC power flow approach can be used to simplify the computational effort. In addition, distribution LMP (DLMP) has been formulated to have both real and reactive power price, under the linearized optimal power flow (OPF) model

Situation Awareness for Smart Distribution Systems

In recent years, the global climate has become variable due to intensification of the greenhouse effect, and natural disasters are frequently occurring, which poses challenges to the situation awareness of intelligent distribution networks. Aside from the continuous grid connection of distributed generation, energy storage and new energy generation not only reduces the power supply pressure of distribution network to a certain extent but also brings new consumption pressure and load impact. Situation awareness is a technology based on the overall dynamic insight of environment and covering perception, understanding, and prediction. Such means have been widely used in security, intelligence, justice, intelligent transportation, and other fields and gradually become the research direction of digitization and informatization in the future. We hope this Special Issue represents a useful contribution. We present 10 interesting papers that cover a wide range of topics all focused on problems and solutions related to situation awareness for smart distribution systems. We sincerely hope the papers included in this Special Issue will inspire more researchers to further develop situation awareness for smart distribution systems. We strongly believe that there is a need for more work to be carried out, and we hope this issue provides a useful open-access platform for the dissemination of new ideas

Power system balancing with high renewable penetration : the potential of demand response

Thesis (S.M. in Engineering and Management)--Massachusetts Institute of Technology, Engineering Systems Division, February 2012."September 2011." Cataloged from PDF version of thesis.Includes bibliographical references (p. 61-62).This study investigated the ability of responsive demand to stabilize the electrical grid when intermittent renewable resources are present. The WILMAR stochastic unit commitment model was used to represent a version of the Danish electricity and heat system with an enhanced level of wind generation. The study found that demand response reduced the marginal operating cost of the electrical system 3%. Demand response reduced CO₂ /SO₂ emissions levels 3% by enabling 11% more generation of wind power. Demand resources representing 25% of nameplate wind power and priced at 150% of a gas turbine's marginal cost were a recommended combination that balanced maximum system improvement at minimal ratepayer impact. The system cost benefits of each study case enabled the calculation of a demand curve representing the system operator's willingness to pay fixed costs for capacity from the pool of operating savings. With demand response, wind generators increased profits, coal plants reduced profits slightly, and natural gas plant profit was cut to almost zero. With high levels of unpredictable renewable resources and limited ability to import power, demand response represents a promising technique to balance the grid at low cost.by D. Karl Critz.S.M.in Engineering and Managemen

A Novel Reinforcement Learning-Optimization Approach for Integrating Wind Energy to Power System with Vehicle-to-Grid Technology

High integration of intermittent renewable energy sources (RES), specifically wind power, has created complexities in power system operations due to their limited controllability and predictability. In addition, large fleets of Electric Vehicles (EVs) are expected to have a large impact on electricity consumption, contributing to the volatility. In this dissertation, a well-coordinated smart charging approach is developed that utilizes the flexibility of EV owners in a way where EVs are used as distributed energy storage units and flexible loads to absorb the fluctuations in the wind power output in a vehicle-to-grid (V2G) setup. Challenges for people participation in V2G, such as battery degradation and insecurity about unexpected trips, are also addressed by using an interactive mechanism in smart grid. First, a static deterministic model is formulated using multi-objective mixed-integer quadratic programming (MIQP) assuming known parameters day ahead of time. Subsequently, a formulation for real-time dynamic schedule is provided using a rolling-horizon with expected value approximation. Simulation experiments demonstrate a significant increase in wind utilization and reduction in charging cost and battery degradation compared to an uncontrolled charging scenario. Formulating the scheduling problem of the EV-wind integrated power system using conventional stochastic programming (SP) approaches is challenging due to the presence of many uncertain parameters with unknown underlying distributions, such as wind, price, and different commuting patterns of EV owners. To alleviate the problem, a model-free Reinforcement Learning (RL) algorithm integrated with deterministic optimization is proposed that can be applied on many multi-stage stochastic problems while mitigating some of the challenges of conventional SP methods (e.g., large scenario tree, computational complexity) as well as the challenges in model-free RL (e.g., slow convergence, unstable learning in dynamic environment). The simulation results of applying the combined approach on the EV scheduling problem demonstrate the effectiveness of the RL-Optimization method in solving the multi-stage EV charge/discharge scheduling problem. The proposed methods perform better than standard RL approaches (e.g., DDQN) in terms of convergence speed and finding the global optima. Moreover, to address the curse of dimensionality issue in RL with large action-state space, a heuristic EV fleet charging/discharging scheme is used combined with RL-optimization approach to solve the EV scheduling problem for a large number of EVs

Methodologies for Frequency Stability Assessment in Low Inertia Power Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen

Three essays on the economics of renewable energy in small island economies

In chapter 1, we introduce mechanism and present results of an integrated investment appraisal of an onshore wind farm for electricity generation in Cape-Verde that is owned and operated by a private investor. From the perspective of the electric utility and the economy, the results of such an ex-ante financial and economic appraisal of wind electricity generation depends critically on one’s view of the expected long-term level of future fossil fuel prices, negotiations of the power purchase agreement (PPA) price and wind capacity factor. In Chapter 2, we investigate the impacts of wind and solar renewable power sources on both electricity generation and planning by employing and applying a cost minimization model in Cyprus. The cost minimization model demonstrates that the use of wind alone and mix of wind and solar power in an electricity generation mix reduces the overall cost of the system. Due to high cost of electricity generation from fuel oil in Cyprus, we conclude that shift toward wind and solar mix of energy sources in Cyprus will have significant impact by means of cost reduction. Therefore, integrating these renewables will essentially contribute to the welfare of Cypriot consumers alongside its environmental and health benefits associated in them. In Chapter 3, we study the impacts of implementing real-time electricity pricing (RTP) in the Cypriot electricity market with and without wind/solar capacities. We use a merit order stack approach to generation investment and operation decisions. Empirical results show that dynamic pricing will increase generation capacity utilization by means of reduction in equilibrium installed capacity reduction and increase in load factors of off-peak plants. These savings are larger at higher demand elasticities. The emissions from electricity generation will potentially increase resulting from increased energy consumption, however. Because wind (solar) availability comes mostly during low (high) demand hours when relatively cleaner (dirtier) plants operate in the system, we find that there is considerable potential for capital cost savings and emission savings from smart metering even with only a small consumer response and at moderate participation in the programme. At the current costs of solar, investing in wind alone will however yield higher bill savings

Flexible Demand in Smart Grids - Modeling and Coordination

The economic analysis of smart grid capabilities needs to incorporate physical boundaries as hard constraints which need to be facilitated by means of flexibility potentials and intelligent dispatching. Due to the distributed nature of demand, economic coordination also needs to be able to facilitate a multitude of individual agents. Questions concerning the modeling and coordination of an active demand side are addressed using tools and techniques from information systems and economics

Bulk electric system reliability simulation and application

Bulk electric system reliability analysis is an important activity in both vertically integrated and unbundled electric power utilities. Competition and uncertainty in the new deregulated electric utility industry are serious concerns. New planning criteria with broader engineering consideration of transmission access and consistent risk assessment must be explicitly addressed. Modern developments in high speed computation facilities now permit the realistic utilization of sequential Monte Carlo simulation technique in practical bulk electric system reliability assessment resulting in a more complete understanding of bulk electric system risks and associated uncertainties. Two significant advantages when utilizing sequential simulation are the ability to obtain accurate frequency and duration indices, and the opportunity to synthesize reliability index probability distributions which describe the annual index variability. This research work introduces the concept of applying reliability index probability distributions to assess bulk electric system risk. Bulk electric system reliability performance index probability distributions are used as integral elements in a performance based regulation (PBR) mechanism. An appreciation of the annual variability of the reliability performance indices can assist power engineers and risk managers to manage and control future potential risks under a PBR reward/penalty structure. There is growing interest in combining deterministic considerations with probabilistic assessment in order to evaluate the “system well-being” of bulk electric systems and to evaluate the likelihood, not only of entering a complete failure state, but also the likelihood of being very close to trouble. The system well-being concept presented in this thesis is a probabilistic framework that incorporates the accepted deterministic N-1 security criterion, and provides valuable information on what the degree of the system vulnerability might be under a particular system condition using a quantitative interpretation of the degree of system security and insecurity. An overall reliability analysis framework considering both adequacy and security perspectives is proposed using system well-being analysis and traditional adequacy assessment. The system planning process using combined adequacy and security considerations offers an additional reliability-based dimension. Sequential Monte Carlo simulation is also ideally suited to the analysis of intermittent generating resources such as wind energy conversion systems (WECS) as its framework can incorporate the chronological characteristics of wind. The reliability impacts of wind power in a bulk electric system are examined in this thesis. Transmission reinforcement planning associated with large-scale WECS and the utilization of reliability cost/worth analysis in the examination of reinforcement alternatives are also illustrated