Guillermo Gutierrez-Alcaraz

Static transmission expansion planning considering uncertainty in demand using BPSO

This paper discusses static transmission expansion planning (STEP) in terms of minimizing the costs of investment and operations. We propose a transmission expansion model that divides into investment and operations problems. We use a binary particle swarm optimization algorithm (BPSO) to solve the investment problem and a DC optimal power flow (DCOPF) to solve the operations problem. We model uncertainty as stochastic demand at each node. A simulated case study numerically evaluates the efficiency of the proposed method.

Load forecasting assessment using SARIMA model and fuzzy inductive reasoning

Accurate load forecasting is critical for power systems planning, control, and operation. Poor forecasting in volatile power markets can have large, detrimental impacts on power system costs and real-time energy acquisition costs from distribution companies. This paper implements and compares two different methodologies for short term load forecasting: a classic statistical model (SARIMA model) and a model based on artificial intelligence (Fuzzy Inductive Reasoning, or FIR, model). A numerical example predicts one week for every methodology and the results are compared for both models.

Pricing reliability service based on end-users choice

Electricity has been considered as a heterogeneous product even in todays electricity marketplace. However, end-customers have different needs and preferences, such that choosing the level of availability that they are willing to pay for becomes a key issue. This paper elaborates the concept by differentiating electricity in multiple availability types and multiple types of classes.

Dynamic pricing and area-time specific marginal capacity cost for distribution investment deferment

Electricity deregulation makes it more feasible to apply differential rates across customers. Dynamic prices can be used to reflect the changes in marginal energy costs of a power system. Some dynamic pricing pilot projects reveal that dynamic prices can actually reduce or shift electricity usage. However, system-load peaks and local-area load peaks could occur at different times. When both peaks are coincident, two objectives can be achieved simultaneously by applying dynamic pricing: total system demand and local demand reduction. However, when these peaks are non-coincident, local demand requires investment. We propose and extended dynamic pricing scheme by taking area- and time-specific marginal distribution capacity costs into account for addressing locational investment deferral. Elasticity model which uses actual test data from pilot projects is adopted to estimate load reduction due to dynamic pricing.

Blocking Strategies against Financial Transmission Right's Market Power

A financial transmission right (FTR) give the holder the right to collect the congestion rent between two nodes, i.e. the difference in price between the two nodes multiplied by the number of contracts held. These contracts are intended to provide a hedging mechanism for market participants, thus reducing risk and inspiring more confidence in todays deregulated wholesale electricity markets. This paper presents a scenario to demonstrate how FTRs can confer market power on an otherwise powerless market player and then shows how adaptive agents can learn to block FTRs market power in a simulated power market.

Determining nodal interruption capacity with reliability assessment method

Demand Side Management (DSM) has been become a popular option for improving electricity system operations and reliability. Moreover, Demand Response (DR) programs provide superior efficiency and flexibility to their users. This paper proposes a method to assess system reliability and evaluate the specific contributions made by spinning reserves (SR) and DR. The method structurally and spatially decomposes the reliability index Expected Energy Not Supplied (EENS) at the nodal level. The method can identify nodes suitable to implementing DR programs. Numerical example of a six-node system demonstrates the effectiveness of the proposed evaluative method.

Optimal fuel and emission acquisition contracts using a supply chain model

For an electric power system to meet the demand for electricity depends not only on subsystems of generation, transmission and distribution, but also on the ability to supply primary energy sources such as natural gas, oil and coal. The effects of a contingency in some of the subsystems, including supply networks, can propagate and affect system operations in terms of availability and price. In a vertically integrated industry the operation of electric power systems has omitted energy supply aspects and the use of financial instruments as part of trade policy. In this paper a supply chain model is used to analyze the acquisition of fuel contracts for generating units. Numerical examples are provided.