MASCEM: electricity markets simulation with strategic agents

Electricity markets are complex environments, involving numerous entities trying to obtain the best advantages and profits while limited by power-network characteristics and constraints.1 The restructuring and consequent deregulation of electricity markets introduced a new economic dimension to the power industry. Some observers have criticized the restructuring process, however, because it has failed to improve market efficiency and has complicated the assurance of reliability and fairness of operations. To study and understand this type of market, we developed the Multiagent Simulator of Competitive Electricity Markets (MASCEM) platform based on multiagent simulation. The MASCEM multiagent model includes players with strategies for bid definition, acting in forward, day-ahead, and balancing markets and considering both simple and complex bids. Our goal with MASCEM was to simulate as many market models and player types as possible. This approach makes MASCEM both a short- and mediumterm simulation as well as a tool to support long-term decisions, such as those taken by regulators. This article proposes a new methodology integrated in MASCEM for bid definition in electricity markets. This methodology uses reinforcement learning algorithms to let players perceive changes in the environment, thus helping them react to the dynamic environment and adapt their bids accordingly.

A data-mining-based methodology for transmission expansion planning

In recent decades, all over the world, competition in the electric power sector has deeply changed the way this sector’s agents play their roles. In most countries, electric process deregulation was conducted in stages, beginning with the clients of higher voltage levels and with larger electricity consumption, and later extended to all electrical consumers. The sector liberalization and the operation of competitive electricity markets were expected to lower prices and improve quality of service, leading to greater consumer satisfaction. Transmission and distribution remain noncompetitive business areas, due to the large infrastructure investments required. However, the industry has yet to clearly establish the best business model for transmission in a competitive environment. After generation, the electricity needs to be delivered to the electrical system nodes where demand requires it, taking into consideration transmission constraints and electrical losses. If the amount of power flowing through a certain line is close to or surpasses the safety limits, then cheap but distant generation might have to be replaced by more expensive closer generation to reduce the exceeded power flows. In a congested area, the optimal price of electricity rises to the marginal cost of the local generation or to the level needed to ration demand to the amount of available electricity. Even without congestion, some power will be lost in the transmission system through heat dissipation, so prices reflect that it is more expensive to supply electricity at the far end of a heavily loaded line than close to an electric power generation. Locational marginal pricing (LMP), resulting from bidding competition, represents electrical and economical values at nodes or in areas that may provide economical indicator signals to the market agents. This article proposes a data-mining-based methodology that helps characterize zonal prices in real power transmission networks. To test our methodology, we used an LMP database from the California Independent System Operator for 2009 to identify economical zones. (CAISO is a nonprofit public benefit corporation charged with operating the majority of California’s high-voltage wholesale power grid.) To group the buses into typical classes that represent a set of buses with the approximate LMP value, we used two-step and k-means clustering algorithms. By analyzing the various LMP components, our goal was to extract knowledge to support the ISO in investment and network-expansion planning.

Demand response management in power systems using a particle swarm optimization approach

Competitive electricity markets have arisen as a result of power-sector restructuration and power-system deregulation. The players participating in competitive electricity markets must define strategies and make decisions using all the available information and business opportunities.

Deregulated expression of selected histone methylases and demethylases in prostate carcinoma

Prostate cancer (PCa), a leading cause of cancer-related morbidity and mortality, arises through the acquisition of genetic and epigenetic alterations. Deregulation of histone methyltransferases (HMTs) or demethylases (HDMs) has been associated with PCa development and progression. However, the precise influence of altered HMTs or HDMs expression and respective histone marks in PCa onset and progression remains largely unknown. To clarify the role of HMTs and HDMs in prostate carcinogenesis, expression levels of 37 HMTs and 20 HDMs were assessed in normal prostate and PCa tissue samples by RT-qPCR. SMYD3, SUV39H2, PRMT6, KDM5A, and KDM6A were upregulated, whereas KMT2A-E (MLL1-5) and KDM4B were downregulated in PCa, compared with normal prostate tissues. Remarkably, PRMT6 was the histone modifier that best discriminated normal from tumorous tissue samples. Interestingly, EZH2 and SMYD3 expression levels significantly correlated with less differentiated and more aggressive tumors. Remarkably, SMYD3 expression levels were of independent prognostic value for the prediction of disease-specific survival of PCa patients with clinically localized disease submitted to radical prostatectomy. We concluded that expression profiling of HMTs and HDMs, especially SMYD3, might be of clinical usefulness for the assessment of PCa patients and assist in pre-therapeutic decision-making.

Phenotypic impact of deregulated expression of class I histone deacetylases in urothelial cell carcinoma of the bladder

Deregulated expression of histone deacetylases (HDACs) has been implicated in tumorigenesis. Herein, we investigated class I HDACs expression in bladder urothelial cell carcinoma (BUCC), its prognostic value and biological significance. Significantly increased transcript levels of all HDACs were found in BUCC compared to 20 normal mucosas, and these were higher in lower grade and stage tumors. Increased HDAC3 levels were associated with improved patient survival. SiRNA experiments showed decrease cell viability and motility, and increased apoptosis. We concluded that class I HDACs play an important role in bladder carcinogenesis through deregulation of proliferation, migration and apoptosis, constituting putative therapeutic targets

Stochastic Framework for Strategic Decision-making of Load-serving Entities for Day-ahead Market

The deregulation of electricity markets has diversified the range of financial transaction modes between independent system operator (ISO), generation companies (GENCO) and load-serving entities (LSE) as the main interacting players of a day-ahead market (DAM). LSEs sell electricity to end-users and retail customers. The LSE that owns distributed generation (DG) or energy storage units can supply part of its serving loads when the nodal price of electricity rises. This opportunity stimulates them to have storage or generation facilities at the buses with higher locational marginal prices (LMP). The short-term advantage of this model is reducing the risk of financial losses for LSEs in DAMs and its long-term benefit for the LSEs and the whole system is market power mitigation by virtually increasing the price elasticity of demand. This model also enables the LSEs to manage the financial risks with a stochastic programming framework.

Role of oxidative stress-induced systemic and cavernosal molecular alterations in the progression of diabetic erectile dysfunction

Background Erectile dysfunction (ED) is a prevalent complication of diabetes, and oxidative stress is an important feature of diabetic ED. Oxidative stress-induced damage plays a pivotal role in the development of tissue alterations. However, the deleterious effects of oxidative stress in the corpus cavernosum with the progression of diabetes remain unclear. The aim of this study was to evaluate systemic and penile oxidative stress status in the early and late stages of diabetes. Methods Male Wistar streptozotocin-diabetic rats (and age-matched controls) were examined 2 (early) and 8 weeks (late) after the induction of diabetes. Systemic oxidative stress was evaluated by urinary H2O2 and the ratio of circulating reduced/oxidized glutathione (GSH/GSSG). Penile oxidative status was assessed by H2O2 production and 3-nitrotyrosine (3-NT) formation. Cavernosal endothelial nitric oxide synthase (eNOS) was analyzed by quantitative immunohistochemistry. Dual immunofluorescence was also performed for 3-NT and α-smooth muscle actin (α-SMA) and eNOS–α-SMA. Results There was a significant increase in urinary H2O2 levels in both diabetic groups. The plasma GSH/GSSG ratio was significantly augmented in late diabetes. In cavernosal tissue, H2O2 production was significantly increased in late diabetes. Reactivity for 3-NT was located predominantly in cavernosal smooth muscle (SM) and was significantly reduced in late diabetes. Quantitative immunohistochemistry revealed a significant decrease in eNOS levels in cavernosal SM and endothelium in late diabetes. Conclusions The findings indicate that the noxious effects of oxidative stress are more prominent in late diabetes. Increased penile protein oxidative modifications and decreased eNOS expression may be responsible for structural and/or functional deregulation, contributing to the progression of diabetes-associated ED.