Nadali Mahmoudi

Wind Power Offering Strategy in Day-Ahead Markets: Employing Demand Response in a Two-Stage Plan

This paper deals with wind power offering strategies in day-ahead markets. A new plan is proposed in which a wind power producer participates in the day-ahead market while employing demand response (DR) to smooth its power variations. In this context, a new DR scheme is presented through which the wind power producer is able to achieve DR by establishing various DR agreements with DR aggregators. The proposed offering plan involves two stages: the first stage clears on the day-ahead market. The wind power producer decides on day-ahead offers as well as DR agreements with the aggregator. The second stage takes place on the balancing market. In a successive approach, the wind power producer determines its energy trading for each period until the whole day is covered. Additionally, proper DR agreements for each period are confirmed here. The proposed plan is formulated in a stochastic programming approach, where its validity is assessed on a case of the Nordic market.

Demand Response Application by Strategic Wind Power Producers

This paper considers a wind power producer playing strategically in a day-ahead market while willing to set demand response (DR) contracts with a DR aggregator. To this end, a bilevel problem including a single leader and two followers is formulated. The wind power producer is the leader aiming at maximizing its profit through offering into a day-ahead market and clearing its deviation in a balancing market. The strategic behavior of the producer in the day-ahead market is modeled through the market clearing process (follower 1). In addition, the DR aggregator behavior is modeled through a revenue function in which the aggregator is able to sell its DR product to the wind power producer, other competitors and the day-ahead market (follower 2). The overall problem is a stochastic mathematical program with equilibrium constraints (MPEC) in which wind power production and imbalance prices are associated with uncertainty. The problem is then transformed into a linear programming approach. A case of the Nordic market is chosen to assess the validity of the given problem.

Developing a scenario-based demand response for short-term decisions of electricity retailers

This paper deals with short-term decisions made by electricity retailers. It is assumed that a retailer aims to minimize the cost of procuring energy from two sources: one is the commonly-used pool market, and the other is the demand response (DR) program proposed in this paper. A reward-based DR is mathematically formulated where the volume of load reduction is modeled as a stepwise function of offered incentives by the retailer. Furthermore, a novel scenario-based participation factor is developed here to take into account the unpredictable behavior of customers. The presented problem is formulated in stochastic programming where its feasibility is evaluated on a realistic case of the Queensland region within the Australian National Electricity Market (NEM). Additionally, we define four distinct cases to study the impact of uncertainties associated with both resources, particularly DR, on short-term decisions of the retailer.

An Innovative Two-Level Model for Electric Vehicle Parking Lots in Distribution Systems With Renewable Energy

With the rapid growth of electric vehicles (EVs) in distribution systems, a new player, called EV parking lot operator (EV PLO), is emerging around the world. Furthermore, the integration of distributed generation in the distribution level, in particular, renewable energy sources, is leading to the establishment of various markets in distribution systems. On one hand, such PLOs aim at managing their EVs within their parking lots to participate in the distribution markets and to maximize their profits. On the other hand, a distribution system operator seeks to minimize the system-wide cost while minimizing renewable power spillage and the side-effects of its intermittency. This interaction inspires the innovative two-level model proposed in this paper. In the first level, a new model is proposed for EV PLOs which models the EVs’ characteristics, including EV owners’ uncertainties, in a reasonably accurate manner. These PLOs are allowed to participate in energy, reserve and regulation distribution markets by optimally managing their EVs. In the second level, a new model is developed to ensure that the technical constraints in the distribution networks are met while minimizing the overall system cost. In addition, this paper evaluates the effects of the penetration level and the placement of wind and solar PV on the offering strategies of EV parking lots, as well as on the overall performance of the distribution systems.

A new trading framework for demand response aggregators

This paper proposes a new trading framework which allows demand response (DR) aggregators to procure DR from consumers and sell it to purchasers. The aggregator obtains DR from the proposed price and incentive-based DR programs. On the other side, the DR outcome is sold to purchasers through the proposed agreements, namely fixed DR contracts and DR options. The presented problem is formulated as a stochastic programming approach, where its feasibility is studied on a case of the Australian National Electricity Market (NEM).

Queensland load profiling by using clustering techniques

Load profiling is essential in power systems operation and planning. Accurate load profiles lead to a better load scheduling as well as load and price forecasting. Clustering techniques are used to provide an enhanced knowledge on electrical load patterns. This paper deals with clustering methods to analyze Queenslands load data. The K-means clustering method is used here, where its accuracy is measured using the clustering dispersion indicator (CDI). This method is applied on the Queensland load curves in 2013, where distinct monthly and yearly load profiles are obtained. In addition, the characteristic of each load profile depending on the day type and weather conditions are analyzed.

Self-Scheduling of Demand Response Aggregators in Short-Term Markets Based on Information Gap Decision Theory

This paper proposes a new self-scheduling framework for demand response (DR) aggregators, which contributes over the existing models in the following aspects. The proposed model considers the uncertainties posed from consumers and electricity market prices. Further, the given model applies the information-gap decision theory (IGDT) in the self-scheduling problem, which guarantees the predefined profit by the aggregator and avoids computational burdens caused by scenario-based methods, such as stochastic programming approaches. The DR aggregator procures DR from two proposed programs, i.e., reward-based DR and time-of-use. Then, the obtained DR is offered into day-ahead and balancing markets. An IGDT-based profit function is proposed, which leads to a bilevel program. The given bilevel model is then transformed into an equivalent single-level model by developing a non-KKT method, which is solved through commercial solvers available in general algebraic modeling system. The feasibility of the problem is studied using a case study with realistic data of electricity markets.

A Comprehensive Model to Integrate Emerging Resources From Supply and Demand Sides

This paper extensively models the interactions of emerging players in future power systems to analyze their impacts on electricity markets. To this end, renewable energy resources are modeled in such a way that wind power poses uncertainty on the supply side, and rooftop photovoltaics add uncertainty to the demand side. Moreover, both uncontrolled and controlled behaviors of individual electric vehicles (EV) in electricity markets are addressed through a new EV model. Further, a comprehensive demand response model considering several customer-driven constraints is developed to undertake the practical constraints of customers. A stochastic market clearing formulation is presented to comprehensively account for the unique features of the given resources while evaluating their impacts. The numerical results clearly show the importance of such modeling in electricity markets to investigate the mutual impacts of emerging resources.

Analysis of Australian power transformer failure and retirement statistics

Analyzing the probability of power transformer failure is challenging because to observe a sufficient number of failures requires a fleet size larger than what is often operated by one utility. Parametric distributions have been applied to small datasets to model failure. However, an assumption is that the failure follows the chosen distribution. Reliability data from 97% of the 6057 utility-owned power transformers operating in mainland Australia and Tasmania were collected and analyzed to develop a more accurate understanding of failure statistics. Information on 564 failures and retirements occurring between 2000 and 2015 was collected, stratified into distribution, subtransmission, and transmission voltage class units, and then analyzed. The useful life of the three different populations was estimated, and the relationship between age and different failure types was investigated.

Optimization of static wind power investment in the Australian national electricity market

This paper studies the Australian market conditions and subsidies in place to promote the growth of wind power through the use of a stochastic linear programming model. This model optimizes the profit obtained for a static investment based on a variety of wind and price scenarios. It is subject to different constraints which focus on the amount of initial capital injected by an investor, selling power through a Power Purchase Agreement (PPA) or using the Market Clearing Price (MCP). The Conditional Value-at-risk (CVaR) risk is also considered in this model to assist in differentiating between a risk-neutral and risk-averse investor. This has been evaluated for Portland on the coast of Victoria, Australia, using current data.