ANN-Based LMP forecasting in a distribution network with large penetration of DG

In recent years, power systems have experienced many changes in their paradigm. The introduction of new players in the management of distributed generation leads to the decentralization of control and decision-making, so that each player is able to play in the market environment. In the new context, it will be very relevant that aggregator players allow midsize, small and micro players to act in a competitive environment. In order to achieve their objectives, virtual power players and single players are required to optimize their energy resource management process. To achieve this, it is essential to have financial resources capable of providing access to appropriate decision support tools. As small players have difficulties in having access to such tools, it is necessary that these players can benefit from alternative methodologies to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), and intended to support smaller players. In this case the present methodology uses a training set that is created using energy resource scheduling solutions obtained using a mixed-integer linear programming (MIP) approach as the reference optimization methodology. The trained network is used to obtain locational marginal prices in a distribution network. The main goal of the paper is to verify the accuracy of the ANN based approach. Moreover, the use of a single ANN is compared with the use of two or more ANN to forecast the locational marginal price.

ANN based support for distributed energy resources scheduling in smart grids

The future scenarios for operation of smart grids are likely to include a large diversity of players, of different types and sizes. With control and decision making being decentralized over the network, intelligence should also be decentralized so that every player is able to play in the market environment. In the new context, aggregator players, enabling medium, small, and even micro size players to act in a competitive environment, will be very relevant. Virtual Power Players (VPP) and single players must optimize their energy resource management in order to accomplish their goals. This is relatively easy to larger players, with financial means to have access to adequate decision support tools, to support decision making concerning their optimal resource schedule. However, the smaller players have difficulties in accessing this kind of tools. So, it is required that these smaller players can be offered alternative methods to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), intended to support smaller players’ resource scheduling. The used methodology uses a training set that is built using the energy resource scheduling solutions obtained with a reference optimization methodology, a mixed-integer non-linear programming (MINLP) in this case. The trained network is able to achieve good schedule results requiring modest computational means.

Contextual intelligent load management with ANN adaptive learning module

With the current increase of energy resources prices and environmental concerns intelligent load management systems are gaining more and more importance. This paper concerns a SCADA House Intelligent Management (SHIM) system that includes an optimization module using deterministic and genetic algorithm approaches. SHIM undertakes contextual load management based on the characterization of each situation. SHIM considers available generation resources, load demand, supplier/market electricity price, and consumers’ constraints and preferences. The paper focus on the recently developed learning module which is based on artificial neural networks (ANN). The learning module allows the adjustment of users’ profiles along SHIM lifetime. A case study considering a system with fourteen discrete and four variable loads managed by a SHIM system during five consecutive similar weekends is presented.

ANN based day-ahead spinning reserve forecast for electricity market simulation

Electricity market players operating in a liberalized environment require adequate decision support tools, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. This paper deals with short-term predication of day-ahead spinning reserve (SR) requirement that helps the ISO to make effective and timely decisions. Based on these forecasted information, market participants can use strategic bidding for day-ahead SR market. The proposed concepts and methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.

ANN based day-ahead ancillary services forecast for electricity market simulation

Adequate decision support tools are required by electricity market players operating in a liberalized environment, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services (AS) represent a good negotiation opportunity that must be considered by market players. Based on the ancillary services forecasting, market participants can use strategic bidding for day-ahead ancillary services markets. For this reason, ancillary services market simulation is being included in MASCEM, a multi-agent based electricity market simulator that can be used by market players to test and enhance their bidding strategies. The paper presents the methodology used to undertake ancillary services forecasting, based on an Artificial Neural Network (ANN) approach. ANNs are used to day-ahead prediction of non-spinning reserve (NS), regulation-up (RU), and regulation down (RD). Spinning reserve (SR) is mentioned as past work for comparative analysis. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.

Province of Upper Canada Land Grant to Ann Cook, 1812

A grant of land to Ann Cook of the Township of Crowland. The land is situated in the township of Wainfleet, in the County of Lincoln, in the district of Niagara. Ann Cook is granted 200 acres in lot no. 32 in the 4th concession in Wainfleet. One of the signatures on the document is illegible but other signatures include: Prideaux Selby, auditor general on April 15, 1812. This is the date that the document was entered into the auditor’s office. Isaac Brock has signed in the upper left hand corner as “Isaac Brock President” which refers to the fact that he was President administering the Government of Upper Canada and Major General commanding the forces within the province. It is also signed on the left hand side by John Macdonell (who is buried at Brock’s Monument). At the time, he was the attorney general.

Ann Eliza Hepburne Rooth fonds, 1837-1897

Ann Eliza Hepburne was born in Chippawa, Ontario, in 1821, to William Hepburne and Susan Shannon. In 1842, she married William Anthony Rooth in St. James Cathedral in Toronto. They continued to live in different parts of the Niagara region, including Drummondville, Welland and Port Colborne. William was the editor and proprietor of the Drummondville Reporter, as well as an accountant and insurance agent, and later worked for the Customs Service in Port Colborne. He died in 1878, and Eliza in 1899. Both are buried in Drummond Hill Cemetery in Niagara Falls, Ontario.

Photograph - Ann and William Sanderson

A framed copy of 2 photographed sketches. Each photograph is 18 cm x 13 cm. Ann and William Sanderson are displayed in this frame.

Photograph - Ann Sophia Sanderson

Photograph of a sketch, 13 cm x 10 cm, of Ann Sophia Sanderson. The photographer was G.F. Maitland of St. Catharines.

Indenture between William and Ann Waters and William B. Winterbottom

Indenture of mortgage with dower between William and Ann Waters of Niagara to William B. Winterbottom of Niagara regarding the easterly half of Lot no. 265 in Niagara. This was recorded in the Register of Forms of Niagara on April 13, 1850, Book A, folios 207-208 - instrument no. 2093, April 1, 1850.

Indenture between Johnson T. Butler, Ann Jane Butler and Henry Rogers

Indenture of bargain and sale between Johnson T. Butler and his wife Ann Jane Butler, both of the Township of Niagara to Henry Rogers of the Township of Niagara regarding part of Lot no. 113 in the Township of Niagara - instrument no. 14467 [it is listed as 14467 and 14466 on different parts of the document], April 13, 1864.

Invitation to the Funeral of Ann Elizabeth Woodruff

Invitation to the funeral of Ann Elizabeth Woodruff, daughter of the late Richard N. Woodruff. The funeral was to be held on Oct. 17, 1871 at her residence in St. Davids, Oct. 16, 1871.

Indenture of bargain and sale between James and Ann Jane Butler of the Town of Niagara to Joseph Augustus Woodruff of the Town of Niagara

Indenture of bargain and sale between James and Ann Jane Butler of the Town of Niagara to Joseph Augustus Woodruff of the Town of Niagara for 50 acres composed of the west half of Lot no. 169 in the Township of Niagara – instrument no. 3309. This was recorded in the Niagara Township Register on Aug. 14, 1851, Book A, Folio 219, Aug. 12, 1851.

Indenture of quit claim between John and Nancy Ann Kerlin of the Township of Grantham to Joseph Augustus Woodruff of the Town of Niagara

Indenture of quit claim between John and Nancy Ann Kerlin of the Township of Grantham to Joseph Augustus Woodruff of the Town of Niagara for 100 acres in the west half of Lot no. 29 in the 2nd Concession in the Township of Nissouri, Middlesex, April 29, 1853.