Determinants of Weekly Yields on Government Securities in India

This paper examines the determinants of the Government yields in India using weekly data from April 2001 through March 2009. The analysis covers Treasury Bills with residual maturity of 15-91 days and Government securities of residual maturity one, five and ten years respectively. The empirical estimates show that a long-run relationship exists between each of these interest rates and the policy rate, rate of growth of money supply, inflation, interest rate spread, foreign interest rate and forward premium. At the same time, the empirical results also show that the relative importance of the determinants varies across the maturity spectrum. The normalized generalized variance decompositions suggest that the policy rate and the rate of growth of high powered money are less important in explaining the proportion of variation in longer term interest rates. The weight of the forward premium also diminishes as we move towards higher maturity interest rates. The inflation rate is also relatively less important in explaining variations in the 10-year rate. The yield spread, on the other hand, is more important in explaining the longer term rates. The results also show that a large proportion of the variation in the rates on the 5-year and 10-year government securities is attributed to the interest rate itself suggesting that the unexplained variation may be a result of cyclical factors that are relatively more important for longer term rates but are not captured by the yield spread and are omitted from the estimations due to the high frequency of data employed.interest rate determination; government yields; cointegration and generalized variance decompositions

A study of bilateral contracts in a deregulated power system network

One of the main objectives of deregulating the electric power industry is to introduce competition in the electricity business and prevent monopolies. The introduction of deregulation has, however, led to confusions in the areas of transmission network loss sharing and the responsibility of generation of reactive power. Because, under deregulation, the business and economic decisions in a power system are made by each individual vendor/utility in a decentralized manner. Each power producing entity operates on the principle of profit maximization by optimizing its production cost of real power, reactive power and the spinning reserve margin. Two methods have been developed to determine a generator's share of transmission loss in a deregulated power system. They are: the Incremental Load Flow Approach (ILFA) and the Marginal Transmission Loss Approach (MTLA). The ILFA employs an iterative load flow technique. The MTLA finds the transmission loss share of a generator by utilizing the marginal rate of transmission loss. Both methods are very straightforward and can be implemented by an electric utility or an Independent System Operator (ISO) with little difficulty. Results obtained from both approaches agree well. The details of the two methods along with some numerical examples have been presented in this thesis. The profit maximization objectives of any generating entity or an IPP not only depends on transmission loss allocation but also on the production levels of real power, reactive power and spinning reserve. A model for profit maximization by a generating entity or an IPP who is interested to sell both real and reactive power is developed and presented in this thesis. In many jurisdictions, a power producer has the option for selling spinning reserve in addition to real and reactive power. A profit maximization model based on the forecasted market price of real power, reactive power and spinning reserve has been developed and presented in this thesis. The model would help a producer to decide the production levels of these three commodities in order to realize the maximum profit. Zero profit conditions have been considered along with the profit maximization model to determine the minimum acceptable price vectors of these three commodities. A small test network and the IEEE 24-Bus Reliability Test System (RTS) have been utilized to conduct studies and illustrate the concepts with numerical examples

Analysis of market incentives on power system planning and operations in liberalised electricity markets

The design of liberalised electricity markets (e.g., the energy, capacity and ancillary service markets) is a topic of much debate, regarding their ability to trigger adequate investment in generation capacities and to incentivize flexible power system operation. Long-term generation investment (LTGI) models have been widely used as a decision-support tool for generation investments and design of energy policy. Of particular interest is quantification of uncertainty in model outputs (e.g., generation projections or system reliability) given a particular market design while accounting for uncertainties in input data as well as the discrepancies between the model and the reality. Unfortunately, the standard Monte Carlo based techniques for uncertainty quantification require a very large number of model runs which may be impractical to achieve for a complex LTGI model. In order to enable efficient and fully systematic analysis, it is therefore necessary to create an emulator of the full model, which may be evaluated quickly for any input and which quantifies uncertainty in the output of the full model at inputs where it has not been run. The case study shows results from the Great Britain power system exemplar which is representative of LTGI models used in real policy processes. In particular, it demonstrates the application of Bayesian emulation to a complex LTGI model that requires a formal calibration, uncertainty analysis, and sensitivity analysis. In power systems with large amounts of variable generation, it is important to provide sufficient incentives for operating reserves as a main source of generation flexibility. In the traditional unit commitment (UC) model, the demand for operating reserves is fixed and inelastic, which does not reflect the marginal value of operating reserves in avoiding the events of load shedding and wind curtailment. Besides, the system-wide reserve constraint assumes that the operating reserve can be delivered to any location freely, which is not true in real-world power system operations. To recognize the value and deliverability of operating reserves, dynamic zonal operating reserve demand curves are introduced to an enhanced deterministic UC model for co-optimizing the day-ahead schedules for energy and operating reserves. In the case study on the RTS-73 test system, comparisons are made between the choices of reserve policies (e.g., single, seasonal or dynamic zones) and of different reserve zonal partitioning methods. Results suggest that the enhanced deterministic UC model produces on average lower operational cost, higher system reliability and higher energy and reserve revenues than the traditional one. Finally, we discuss future directions of methodological research arising from current energy system challenges and the computer models developed for better understanding of the impacts of market incentives on power system planning and operations

Optimal hedging in European electricity forward markets.

This article is concerned with modeling the dynamic and distributional properties of daily spot and forward electricity prices across European wholesale markets. Prices for forward contracts are extracted from a unique database from a major energy trader in Europe. Spot and forward returns are found to be highly non normally distributed. Alternative densities provide a better fit of data. In all cases, conditional heteroscedastic models are used with success to specify the data generating process of returns. We derive implications from the relation between spot and forward prices for the evaluation of hedging effectiveness of bilateral contracts. The relation is parametrized by the mean of multivariate GARCH models possibly allowing for dynamic conditional correlation. Because correlation between spot and forward returns is very low on each market, derived optimal hedge ratios are insignificant. We conclude to a great inefficiency for forward markets at least for short-term horizon. Hedging effectiveness is not improved, for our data, through the use of dynamic correlation models.Electricity; multivariate GARCH; dynamic correlation models; non Gaussian densities; optimal hedging; cross-hedging;

Impact of optimally placed VAR support on electricity spot pricing

In view of deregulation and privatization processes, electricity pricing becomes one of the most important issues. The increases in power flows and environmental constraints are forcing electricity utilities to install new VAR equipment to enhance network operation. In this thesis a nonlinear multi-objective optimization problem has been formulated to maximize both social welfare and the maximum distance to collapse point in an open power market using reactive support like Static Var Compensator (SVC). The production and consumption costs of reactive power are intended to provide proper market signals to the electricity market agents. They are included in the multi-objective Optimal Power Flow (OPF) coupled with an (N-1) contingency criterion which is based on power flow sensitivity analysis.;Considering the cost associated with the investment of VAR support, placing them at the optimal location in the network is an important issue. An index to find the optimal site for VAR support considering various technical and economical parameters based on Cost Benefit Analysis (CBA) is proposed. The weights for these parameters are computed through an Analytic Hierarchy Process (AHP). A new approach of transmission pricing calculation taking VSC-OPF based multi-objective maximization as the objective and studied the impact of SVC on it. The integrated approach is illustrated on a 6-bus and a standard IEEE 14-bus test systems and shows promising results

Modeling of Utility Distribution Feeder in OpenDSS with Steady State Impact Analysis of Distributed Generation

With the deregulation of the electric power industry and the advancement of new technologies, the attention of the utilities has been drawn towards adopting Distributed Generation (DG) into their existing infrastructure. The deployment of DG brings ample technological and environmental benefits to the traditional distribution networks. The appropriate sizing and placement of DGs which generate power locally to fulfill consumer demands, helps to reduce power losses and avoid transmission and distribution system expansion.;The primary objective of this thesis is to model a utility distribution feeder in OpenDSS. Studies are conducted on the data obtained from American Electric Power utility. This thesis develops models for 12.47 kV (medium voltage) distribution feeders in OpenDSS by utilizing the existing models in CYMDIST. The model conversion is achieved by a detailed one-to-one component matching approach for multi phased lines, conductors, underground cables, loads, regulators and capacitor banks. The power flow results of OpenDSS and CYMDIST are compared to derive important conclusions.;The second major objective is to analyze the impacts of DG on distribution systems and two focus areas are chosen, namely: effect on voltage profiles and losses of the system and the effects on power market operation. To analyze the impacts of DG on the distribution systems, Photovoltaic (PV) system with varying penetration levels are integrated at different locations along the developed feeder model. PV systems are one of the fastest growing DG technologies, with a lot of utilities in North America expressing interest in its implementation. Many utilities either receive incentives or are mandated by green-generation portfolio regulations to install solar PV systems on their feeders. The large number of PV interconnection requests to the utilities has led to typical studies in the areas of power quality, protection and operation of distribution feeders. The high penetration of PV into the system throws up some interesting implications for the utilities. Bidirectional power flow into a distribution system, (which is designed for one way power flow) may impact system voltage profiles and losses. In this thesis, the effects of voltage unbalance and the losses of the feeder are analyzed for different PV location and penetration scenarios.;Further, this thesis tries to assess the impact of DG on power market operations. In a deregulated competitive market, Generation companies (Genco) sell electricity to the Power exchange (PX) from which large customers such as distribution companies (Disco) and aggregators may purchase electricity to meet their needs through a double sided bidding system. The reliable and efficient operation of this new market structure is ensured by an independent body known as the Independent System Operator (ISO). Under such a market structure, a particular type of unit commitment, called the Price Based Unit Commitment (PBUC) is used by the Genco to determine optimal bids in order to maximize its profit. However, the inclusion of intermittent DG resources such as wind farms by the Gencos causes uncertainty in PBUC schedules. In this research, the effects of intermittency in the DG resource availability on the PBUC schedule of a Genco owning a distribution side wind farm are analyzed

Electricity Sector Reform in Developing Countries: A Survey of Empirical Evidence on Determinants and Performance

This paper reviews the empirical evidence on electricity reform in developing countries. We find that country institutions and sector governance play an important role in success and failure of reform; reforms appear to have increased operating efficiency and expanded access to urban customers; they have to a lesser degree passed on efficiency gains to customers, tackled distributional effects, or improved rural access. Moreover, some of the literature is not methodologically robust or on a par with general development economics literature and findings on some issues are limited and inconclusive while some important areas are yet to be addressed. Until we know more, implementation of reforms will be more based on ideology and economic theory rather than solid economic evidence.The World Bank Electricity Research Programme and the CMI Electricity Project (IR-45

Stochastic Optimization for Integrated Energy System with Reliability Improvement Using Decomposition Algorithm

As energy demands increase and energy resources change, the traditional energy system has been upgraded and reconstructed for human society development and sustainability. Considerable studies have been conducted in energy expansion planning and electricity generation operations by mainly considering the integration of traditional fossil fuel generation with renewable generation. Because the energy market is full of uncertainty, we realize that these uncertainties have continuously challenged market design and operations, even a national energy policy. In fact, only a few considerations were given to the optimization of energy expansion and generation taking into account the variability and uncertainty of energy supply and demand in energy markets. This usually causes an energy system unreliable to cope with unexpected changes, such as a surge in fuel price, a sudden drop of demand, or a large renewable supply fluctuation. Thus, for an overall energy system, optimizing a long-term expansion planning and market operation in a stochastic environment are crucial to improve the system\u27s reliability and robustness. As little consideration was paid to imposing risk measure on the power management system, this dissertation discusses applying risk-constrained stochastic programming to improve the efficiency, reliability and economics of energy expansion and electric power generation, respectively. Considering the supply-demand uncertainties affecting the energy system stability, three different optimization strategies are proposed to enhance the overall reliability and sustainability of an energy system. The first strategy is to optimize the regional energy expansion planning which focuses on capacity expansion of natural gas system, power generation system and renewable energy system, in addition to transmission network. With strong support of NG and electric facilities, the second strategy provides an optimal day-ahead scheduling for electric power generation system incorporating with non-generation resources, i.e. demand response and energy storage. Because of risk aversion, this generation scheduling enables a power system qualified with higher reliability and promotes non-generation resources in smart grid. To take advantage of power generation sources, the third strategy strengthens the change of the traditional energy reserve requirements to risk constraints but ensuring the same level of systems reliability In this way we can maximize the use of existing resources to accommodate internal or/and external changes in a power system. All problems are formulated by stochastic mixed integer programming, particularly considering the uncertainties from fuel price, renewable energy output and electricity demand over time. Taking the benefit of models structure, new decomposition strategies are proposed to decompose the stochastic unit commitment problems which are then solved by an enhanced Benders Decomposition algorithm. Compared to the classic Benders Decomposition, this proposed solution approach is able to increase convergence speed and thus reduce 25% of computation times on the same cases